Starting with early rule-based programmatic trading in the 1970s and 1980s, finance firms have long applied statistics and computing to markets. In the 1990s and 2000s, machine learning and neural networks added sophistication.

For example, hedge funds like Renaissance Technologies hired PhDs to use AI for pattern recognition. Today, we stand at a new inflexion point with generative AI and large language models that can process massive streams of text and data and even suggest novel trading ideas. As one Wharton finance expert notes, AI’s evolution “from algorithmic trading to personalised advice” has made finance “fertile ground for AI innovation.”

Applications of AI in finance

AI is now embedded in many financial processes. Broadly, AI serves in trading, analysis, and operations. In trading, automated systems place orders faster than any human can. High-frequency trading algorithms, often powered by machine learning (ML), make thousands of small trades every second to exploit tiny price discrepancies. Many of the largest trading venues are dominated by such “automated trading” in highly liquid assets. In other domains, AI systems read and summarise information.

For example, NLP tools scan newsfeeds and social media to gauge market sentiment, a process known as sentiment analysis. A sudden burst of negative tweets about a company might trigger selling by algorithms. In risk modelling and compliance, AI churns through vast data to calculate creditworthiness or portfolio risk in real time.

Advisors and insurers use AI to predict defaults or claims, while banks deploy chatbots to handle customer queries. In short, AI touches everything from trade execution to loan approvals and is effectively “democratising” access to analytics that only big institutions once had.

The influence of AI and algorithms is clearest in a few headline-grabbing episodes. In January 2021, the GameStop saga showed the power of social sentiment and automated strategies. A surge of retail traders on Reddit’s WallStreetBets sent the share price of the video-game retailer GME skyrocketing over several days.

Hedge funds that had short positions in the stock rushed to close them. Eventually, trading apps temporarily halted trading, igniting a political firestorm. Researchers note that “retail investors using the Robinhood platform” collectively drove the sharp price swing. Although that episode was driven by human coordination online, it attracted algorithmic responses, with some trading bots detecting the rapid price trend and either piling in or pulling out, amplifying volatility.

AI-driven trading has also featured in the activity of quantitative hedge funds. Firms like Renaissance Technologies, Two Sigma, DE Shaw, and others have long used machine learning to devise strategies. A 2019 survey identified those four as pioneers in AI-driven investing. These firms process vast alternative datasets, from satellite imagery of retail parking lots to aggregated price patterns, looking for subtle predictive signals.

For example, AI can spot that a retail chain’s lawns are greener or read thousands of local news sites to update earnings estimates. In late 2022, Reuters reported Renaissance’s quant funds using models to target returns. Although strategies are secretive, experts agree that AI “provides a competitive advantage” in systematic trading.

AI and social media can also combine in troubling ways. Studies and news accounts warn of sentiment manipulation using bots. In a recent report, experts imagined hundreds of AI-generated social media profiles pushing a narrative about a stock. Real people reacting to the buzz drive the price up or down, while those who detect the narrative profit.

The danger is that neither the promoter nor some of the manipulators even realise they’re part of a larger AI-driven scheme, making enforcement hard. In practice, regulators have seen smaller-scale attempts in crypto and DeFi, where “malicious actors…deploy AI bots” on platforms like Telegram to hype assets.
These examples highlight how automated sentiment analysis and engagement can influence markets, sometimes legitimately, with bots surfacing true trends and at other times through coordinated pumping.

Speed, scale and smarter markets

The attraction of AI in finance is clear, as it does things humans cannot. Speed and automation are paramount. Machines can execute orders in microseconds and monitor markets around the clock, far faster than any trading floor. This rapid processing tightens bid-ask spreads and improves liquidity in normal times.

As the IMF notes, technology has “improved price discovery, deepened markets, and often dampened volatility” in normal periods. AI also excels at scalability and data processing. Financial markets generate enormous volumes of data on prices, news, social posts, filings, and satellite images, and AI can sift through it all.

Advanced neural networks and LLMs (Large Language Models) can turn unstructured text into structured signals. For instance, a generative model can instantly read a regulatory filing or earnings call transcript, flagging risks or opportunities. The IMF notes that generative AI lets investors “process very large amounts of unstructured, often text-based, data,” which can improve forecasts and price accuracy.

Another benefit is pattern recognition and precision. AI algorithms can spot complex statistical patterns that humans cannot see, such as nonlinear relationships or high-dimensional correlations.

In portfolio management, for example, deep-learning models and reinforcement learning (RL) can adapt trading rules over time. Quantitative analysts now use RL to optimise asset allocation dynamically, a method well-suited for constantly shifting markets.

These models “identify complex patterns in large datasets” by using millions of parameterised rules, going far beyond traditional formulae. In effect, AI can tailor strategies to ever-changing conditions, learning minute details of market microstructure.

This leads to efficiency and consistency, and routine tasks like compliance checks or customer service get automated via RegTech tools and chatbots, freeing humans for higher-level thinking. In trading, even a tiny improvement can be valuable. A recent AI pilot by HSBC reportedly found a quantum-enhanced model that improved trade-fill predictions by 34% over classical methods.

Finally, AI can open new markets and lower costs. According to the IMF, AI tools are reducing barriers to entry and making it feasible for smaller firms or even individuals to analyse less-liquid markets like emerging debt or certain commodities. By automating research, coding, and data gathering, generative AI might lower the expertise needed to trade exotic assets.

In retail finance, AI-powered robo-advisors have democratised wealth management. One report notes that about half of retail investors say they would use ChatGPT or similar AI to choose or rebalance investments.

This suggests AI is making advanced analysis available to “anyone,” not just Wall Street. Overall, proponents argue these gains, faster reactions to news, more thorough analysis, and automation, should make markets more efficient and investors more informed.

Herding, black boxes and volatility

AI in finance may sound like an interesting and exciting concept, but it is not risk-free. A key concern is model correlation or “monoculture.” When many firms use similar data and algorithms, their trades tend to move together. Regulators and economists warn that this can amplify swings.

For example, if numerous deep-learning models all see a similar signal, they might simultaneously sell stocks, creating a cascade. The Bank of England and the SEC have warned that advanced AI’s “hyper-dimensionality” and shared data sources could lead to just a few dominant models or data providers. In practical terms, a “monoculture” of strategies can increase market correlations and herding. In stressed markets, this may cause liquidity to evaporate suddenly.

A recent IMF analysis noted that many algorithmic funds include safety mechanisms that can all activate at once, causing feedback loops. The 2010 “Flash Crash” is a cautionary example of an automated sell order in one market leading to a chain reaction, briefly knocking 1,000 points off the Dow within minutes.

Though that crash predated today’s AI, it illustrates the danger of automated systems acting in unison. Experts now worry AI-driven trading could produce even faster and larger moves.

Closely related is model opacity and explainability. Modern AI models are often “black boxes” that even their designers cannot fully explain how a specific trading decision was reached. This poses problems for oversight. If an AI fund suddenly accumulates a large position in an obscure asset, regulators might not understand why.

The IMF notes that market participants insist on human oversight and explainable strategies, avoiding purely “black box” approaches. Likewise, a recent Sidley (law firm) report warns that deep-learning and reinforcement-learning systems can have “emergent behaviour” that current market rules aren’t built to catch.

For example, if an AI learnt to detect fraud or manipulate prices in some non-obvious way, standard surveillance systems might miss it. The opacity also raises ethical concerns. How do we verify that AI decisions are fair and unbiased? Finance is littered with historical biases, so an AI trained on past records might perpetuate discrimination. Wharton researchers point out that “bias in AI models is particularly pertinent” in finance, especially lending and insurance.

There are also privacy and manipulation issues. Bad actors can use AI to tailor scams or spread disinformation. SEC Chair Gary Gensler warns that AI-driven narrowcasting can facilitate fraud by zeroing in on individuals’ vulnerabilities. Indeed, regulators have already flagged concerns about AI-generated “deep fakes” of company announcements or rumours that could jolt markets.

Finally, there is the risk of systemic volatility. Many worry that AI might make crises worse by speeding up decision-making. In turbulence, when computers pile into or out of trades in milliseconds, prices can swing violently.

The Sidley report cites the IMF in noting that many AI strategies include circuit-breaker logic that all trigger together under unprecedented moves, risking a sudden freeze of liquidity. In other words, while AI may “damp down” routine volatility by making markets more efficient, it might also set the stage for faster, sharper shocks. Small errors or adversarial attacks on widely used models could propagate quickly across markets. There’s also a concentration risk, and just a few tech firms provide the most advanced AI services and cloud infrastructure, so outages or cyberattacks could disrupt financial systems more broadly.

Governance meets technology

Awareness of these issues is growing. Governments and regulators worldwide are moving to govern AI in finance. In the EU, for example, the new AI Act will classify many financial AI systems as “high-risk” and impose strict obligations.

Practices like AI-based credit scoring or risk pricing will have to meet transparency, data quality, and audit requirements. The stated goal is “consistency and equal treatment in the financial sector.”

Financial institutions are also starting to set their own AI governance. Many banks now require human sign-off on automated strategies. Investment funds maintain “model risk management” teams to test how strategies behave under stress. After the GameStop episode, social platforms began cracking down on stock-promo groups. And financial regulators update rules in light of faster trading speeds.

Still, experts say more will be needed. For example, regulators worry about a lack of transparency when nonbanks use cutting-edge AI outside full supervision. There are calls for international coordination, like the Financial Stability Board surveying AI preparedness in different countries.

Another trend on the horizon is quantum computing. While today’s AI uses classical computers, quantum machines promise even more power. If scalable quantum computers arrive, they could revolutionise optimisation and simulation problems in finance.

Banks are already experimenting. In 2025, HSBC announced a pilot with IBM showing that a quantum algorithm could predict bond trade outcomes 34% better than classical methods.

UBS, Citigroup, and others are researching quantum for portfolio optimisation and risk analysis, and analysts estimate the “quantum technology” market could reach $100 billion by 2030.

In plain terms, quantum computing could solve certain portfolio or pricing problems much faster than today’s fastest supercomputers. However, practical quantum advantage remains in early stages, and much of that promise is years away. Even so, finance leaders like HSBC’s quantum head call this a “new frontier” in computing for markets.

Tale of two traders

The AI wave affects big institutions and small investors differently. Large financial firms such as banks, hedge funds, and trading firms have the resources to develop sophisticated AI. They run vast data centres, hire machine-learning experts, and deploy cutting-edge models.

These institutional players have led the AI adoption for over a decade as they’ve used automated algorithms in HFT and complex derivatives trading. They also invest in AI for risk management and compliance. Because of their scale, they have an edge in computing speed and data access.

Retail investors have lagged but are catching up. The same chatbots and analysis tools that institutions use are now available to individuals in a lighter form. As one industry report noted, about half of retail investors say they would use AI tools to pick or adjust investments, and around 13% already do. User-friendly platforms now offer AI-driven advice and portfolio screening.

For example, retail-friendly robo-advisors automate investing for individuals with modest accounts. Even individual day traders are experimenting with off-the-shelf AI bots or sentiment-tracker apps. Indeed, the widespread curiosity about ChatGPT and AI has “democratised” access to analysis once reserved for big banks. One former UBS analyst remarked that using ChatGPT for stock research was akin to “replicating many workflows” of an expensive Bloomberg terminal.

Balancing innovation and stability

AI’s role in finance is growing fast. As the IMF puts it, generative AI is the “latest stop on a journey” where technology incrementally improves markets. Its benefits in faster processing, new insights from data, and lower costs have already transformed many aspects of trading and investment.

But the journey is not without bumps. Our analysis shows that there are real risks that correlate with AI models, as they could unintentionally synchronise market behaviour, create opaque algorithms, trigger flash crashes, and mislead investors.

Addressing these issues will require vigilance and innovation on their own part. Regulators are awakening to the challenge, calling for AI governance frameworks and updating rules for our faster, more complex markets.

Financial firms are instituting controls on things like explainability requirements and kill switches for trading bots. Meanwhile, new technologies on the horizon, like quantum computing, promise even more powerful tools.

In the end, the AI transformation in finance mirrors other revolutions by creating opportunities and pitfalls. The central question will be how these systems are deployed. Used wisely, they can make markets more efficient and accessible to more people. Used recklessly, they could amplify our worst crashes or widen inequalities.

For investors and policymakers alike, the task is to harness AI’s ingenuity while keeping our collective financial system resilient. Industry leaders must ensure AI markets remain “transparent, fair, and inclusive,” even as the algorithms get ever smarter.

The post AI drives change in global markets appeared first on International Finance.

While some meme coins come with a market value (which also makes them convertible currencies that can be used in real-world transactions), most of them don’t often have utility, such as being used to pay blockchain participants for doing work for the blockchain. For example, ether is used to pay validators for verifying transactions on the Ethereum blockchain.

When discussing meme coins, the first name that often comes to mind is Dogecoin (DOGE), which has a market cap of USD 14.42 billion as of September 2024. However, other notable coins include SHIB, PEPE, WIF, BONK, and FLOKI. In today’s episode of “Start-up of the Week,” International Finance will feature a venture called Neverless. This company aims to simplify the process for aspiring investors to start trading cryptocurrency, with a particular emphasis on providing access to small-cap tokens.

Knowing The Start-Up In Detail

The crypto startup was founded by three former executives at British fintech giant Revolut in 2022. One of the founders, Phuc To, was Revolut’s global head of product, and was in charge of the company’s crypto project back in 2021. His colleague Mikael Peydayesh was the head of core payments at Revolut, and later became the head of premium plans, and another co-founder Arthur Johanet was the head of card payments for a while before he went on to lead Revolut’s cryptocurrency department.

While crypto exchanges have greatly simplified the onboarding experience for new users over the years, the trio believed it could still be improved, so they teamed up to create a new app with one vision: making cryptocurrencies more accessible. Neverless lets crypto investors buy over 400 crypto tokens from the start-up’s app using Apple Pay or Google Pay. In addition to the most common cryptocurrencies, the start-up has chosen to offer access to meme coins and relatively rare coins with low trading volumes.

Buying these small-cap tokens can be challenging, as the investors usually need to find a crypto exchange that lists them. Alternatively, they can swap tokens on a decentralised exchange, which can be complicated if the investors don’t understand how decentralised applications (dApps) work. When it comes to people buying tokens with low trading volumes, they can face things like varied pricing from one trading venue to another, apart from having a large spread between the buying and selling prices. Neverless claims it can seamlessly route trades to the right trading platform to get its users the best prices.

On some cryptocurrencies (BTC, ETH, DOGE, SOL, XRP, and AVAX), Neverless generates interest that is passed on to the users. The company also offers automated trading strategies that revolve around high-frequency arbitrage and market-making. Neverless can take a share of the returns generated from these yield-generating products.

The company has secured a MiFID (Markets in Financial Instruments Directive) license, which means that it is a regulated financial firm in Europe. It will have to comply with the Markets in Crypto-Assets (MiCA) regulation when it comes into force in the coming days. Earlier in 2024, the start-up raised USD 6.7 million in a seed round led by Lakestar and Connect Ventures.

As of December 2024, Neverless has transformed itself as an institutional-grade platform to grow the crypto investors’ capital, with the latter getting the opportunities to buy meme coins, trade 500-plus cryptos for free, apart from earning automatically up to 7.78% AER (Annual Equivalent Rate) on savings, investing like hedge funds in 11.16% AER Strategies and zero-fee trading, which results in crypto investors earning higher returns on their assets (six times higher than the highest easy-access rate of banks).

Here Are The Products

In terms of facilitating crypto trading, Neverless allows investors to open their online accounts in less than two minutes, from where they can buy their meme coins through Apple or Google Pay, while not hurting their purses on things like deposits or excess fees.

If these same people are buying coins from other platforms, they immediately start at a loss of up to 1.49%, due to odds not being in their favour. However, Neverless assures profits for crypto traders as by spending USD 1000, they get 0.01074 BTC in return, higher than what established exchanges like Binance and Coinbase are offering now. The traders automatically earn 3.04% annual interest on Bitcoin with Neverless, with no lock-up period.

Next is Neverless’ “Strategies Account,” which the start-up dubs as a crypto investor’s pocket hedge fund. By investing in the fund, one will get a return of 11.17% AER (Annual Equivalent Rate that shows what the interest rate would be if interest was paid and compounded annually). Returns through the “Strategies Account” make investors’ long-term earnings exponential.

Unlike traditional hedge funds, “Strategies Account” gives people options to invest in crypto as little as they like, without worrying about locking in their funds for long periods. Generally, hedge funds involve investments based on “prediction,” while trying to forecast market movements, highs and lows. However, predictions often don’t come true, resulting in those investments failing.

Neverless Strategies differentiates itself from traditional hedge funds by helping investors to make money in real-time with real data. The start-up’s algorithm scans the live markets for inefficiencies and profits from them. It detects small price differences of the same asset in different venues, or quotes to both buyers and sellers at different prices. All the trades take place in nanoseconds, while ensuring consistent returns at lower risks, irrespective of the market movements.

Giving Paramount Importance To Data Safety

While most of the 21st century businesses are relying on AI-powered bots to answer customer queries, Neverless has put its money on “Human Intelligence,” when it comes to answering things in a transparent and crystal-clear manner, especially making investors understand that “No Return is Risk-Free.” To back things up, the start-up has even installed an in-app risk dashboard, updated in real-time.

The venture’s core team comes from a bank (Revolut) that looked after 40 million-plus customers and more than 17 billion euro in assets. All of Neverless’s solutions are designed and operated as per the industry-leading security protocols like biometrics and multi-factor authentication. Passcodes never get stored in plain text and irreversibly hashed using a competition-winning algorithm so that no one can read or decrypt them, not even Neverless staffers.

Registered as a Virtual Asset Service Provider in multiple European Union countries, Neverless only works with the world’s best financial crime technology firms.

“Data are encrypted at rest using AES-256 and in transit using TLS 1.2 or greater. We leverage Google Cloud for the physical security of our servers. Our staff is thoroughly vetted and regularly trained on data protection. Minimum access is given on a need-to-know basis and frequently reviewed,” the start-up noted.

The post Start-up of the Week: Neverless is here to make meme coins easy to buy appeared first on International Finance.

The proposed sovereign wealth fund of South Africa will be a state-owned investment fund investing in financial assets such as stocks, bonds, real estate and precious metals. It also includes alternative investments such as private equity funds or hedge funds.

South Africa’s proposed sovereign wealth fund will aim to secure and develop the country’s national endowment. 

In recent years, South Africa has made significant progress in retail financial inclusion. Now 90 percent of South African adults have access to at least one form of financial service, media reports said. 

President Cyril Ramaphosa in his speech said that, “The National Treasury and the SA Reserve Bank are working together to ease pressure on business and consumers. We have decided to establish a sovereign wealth fund as a means to preserve and grow the national endowment of our nation, giving practical meaning to the injunction that the people shall share in the country’s wealth. We are also proceeding with the establishment of a state bank as part of our effort to extend access to financial services to all South Africans.” 

However, no timeline has been announced for the establishment of the sovereign wealth fund. Finance Minister Tito Mboweni is expected to share details on the plan in his budget speech on February 26. 

The post South Africa to set up wealth fund, state bank to improve financial access appeared first on International Finance.

5th September 2013

Consider this scenario, share prices on Philippine Stock Exchange opened higher last Friday (August 3oth), extending Thursday’s rebound as positive sentiment over higher than expected gross domestic product (GDP) output in the second quarter. The PSE was up 36.80 points or 0.62 percent to 5,981.01. The surge in the prices of stocks was due to end of the month “window dressing”.

An analyst in the PSE said “We see a continued rally today due to a combined better than expected GDP growth in the second quarter and month end window dressing”.

Why is the term window dressing used and what does it imply? What are the possible repercussions of window dressing? and is it ethical for companies to window dress their holdings? – International Finance Magazine with data and factual inputs from the Wall Street Journal explains the concept of Window Dressing, the different strategies employed in Window Dressing, how does it benefit hedge fund managers, movement of a particular stock which employs the window dressing strategy and the SEC crack down on banks for understating their risk and avoiding a replica of the sub prime crisis.

What is Window Dressing?

Investopedia defines Window Dressing as “a strategy used by mutual fund managers near the year or quarter end to improve the appearance of the portfolio/fund performance before presenting to clients or shareholders. In a case of window dressing, the fund manager will sell stocks with large losses and purchase high flying stocks near the end of the quarter. These securities are then reported as part of the fund’s holdings”.

Theoretically, window dressing may make a fund appear more attractive, but the stock will tend to decline after a certain period.

For Example: Let us assume that Lucky and Co wants its balance sheet to look attractive to potential acquirers. It might do some window dressing by announcing higher sales projections in the quarter ended, double the cash in hand position and sell certain losing positions so as to display only positions that have gained in value. Financial institutions also employ another strategy of window dressing wherein they dispose off the debt from their balance sheet near the end of the quarter in a temporary manner. This will make the bank/financial institution appear to have less leverage than it actually did.

While the former is considered legal the latter is illegal.

Quoting another example that appeared in The Wall Street Journal, the stock of Iridex Corp., a maker of lasers used to treat visual ailments had been hovering around $ 3.43 all day on June 29^th., but five minutes before the market could call it a day, it took off, moving from $ 3.65  to $ 3.80. Less than half a second before the trading day and the calendar quarter ended, Iridex jumped 4 percent to $ 4.17, an unprecedented increase of 22 percent for the day. The next trading day, July 2^nd, Iridex dropped by 10 percent and did not reach $ 4 until late October.

Wall Street Journal Analysis

The unusual rise and fall of Iridex is not unusual, a Wall Street Journal analysis of daily trading in roughly 10,000 stocks since 2004 found that on the final trading day of each quarter, there was a sharp increase in the number of stocks that beat the market by at least five percentage points, then trailed it by three points or more the next day.

“Marking the Close or Portfolio Pumping”

Marking the Close or Portfolio Pumping is a form of window dressing wherein a variety of techniques are employed by asset managers wait until the waning  moments of the quarter to bid aggressively for more shares of a stock they already own, which drives up the value of their entire position in the stock. This activity boosts their performance at the very moment when they report results, making their funds look more appealing to potential investors. Even if the jump in stock price is only temporary, the managers can attract new money and earn higher fees. The Journal’s analysis compared the performance of those 10,000 stocks to the one day return of the S&P 500 index, the results were baffling, on days that didn’t end the quarter, an average of 217 stocks beat the index by 5 percentage points then trailed by at least 3 the next day. But on the final trading days of the quarters, an average of 280 stocks did.

Window dressing occurs more frequently in small, thinly traded stocks, whose shares tend to swing rigorously, according to trading and securities-law experts. With reference to the example quoted from The Wall Street Journal, Iridex has a market value of $ 36 million, making it  about 1/20^th the size of the average U.S. “micro map” small stock. The day before Iridex’s sudden rise in value on June 29^th the stock had been down 22 percent for the quarter, its last minute surge helped it close the quarter down at 5.4 percent. The company’s market value rose to more than $ 37 million, from less than $ 31 million the day before the window dressing.  The firm got richer by $ 7 million in the last five minutes of June 29^th.

Who are the Gainers?

Hedge funds make millions of dollars through window dressing; a stock jump in the closing hours of can benefit a hedge fund manager‘s fees.  Hedge funds typically collect annual fees equivalent to 2 percent of total assets and pocket 20 percent of profits. Regulators and industry experts say a fund that already has a substantial holding in a small stock can drive up the value of its entire position by purchasing as few as 100 additional shares at a premium to their market price in the final moments of trading at the end of a measurement period.

Richard Sinise, a portfolio manager at Kennedy Capital says that for hedge funds whose fees are linked to performance, “there can be that urge on the last day to put all your cash into your stocks and get them to the price you think they should be at”

SEC cracks down on banks for Window Dressing

The SEC in 2010, proposed rules designed to stop banks and other companies evading information to investors on their short term borrowings. The proposed disclosure rules would require companies to report their average and maximum short term borrowings, as well as the debt outstanding at the end of each reporting period, Directors would be held liable to explain for any significant discrepancies between the debt levels.

The post Understating Riskiness Through Window Dressing appeared first on International Finance.

26th August 2013

Last month, two hedge funds took a controlling stake in part of the Co-operative Bank’s Debt.  Aurelius Capital Management and Silver Point Capital have built stake in one part of the bank’s loans, putting them in a strong position as the lender works to fill a £ 1.5 billion capital hole. Argentina did settle with the vast majority of bond holders but the vulture funds that bought funds for pennies on the dollar from a small minority of the bondholders used their immense influence on American politicians – to whose campaigns and political organisations they contribute generously to stop both the IMF and the U.S. Treasury from filing opinions adverse to their interests with the U.S. Supreme Court. To their advantage, the U.S. Supreme Court affirmed the prior decisions of the lower courts will stand in place for all future sovereign debt negotiations in the hands of a powerful and wealthy vulture fund speculators, these vested interests use any measure to force governments to pay them vast sums, even if it is against the national interests.

In 1999, a vulture fund called Donegal International bought a debt owned by Zambia for a knockdown price of $ 3.3 million. Most of Zambia’s debt was cancelled and the country began to save $ 40 million a year when they stopped paying loans to the IMF and the World Bank. After Zambia received this debt relief, Donegal sued the African Nation for $ 55 million and in April 2007, the court ruled that Zambia must pay $ 15.4 million, 65 percent of the debt relief that was specifically directed for development projects. It was a huge profit for the vulture fund and a theft from the poorest Zambians. The U.K Royal Court of Justice in London ruled that Zambia must pay $ 15.4 million and also a share of legal costs to Donegal International, which had sued the African nation for payment of funds.

What is Vulture Fund?

Academicians and financial experts describe vulture funds “as funds that pounce on a state like Vulture on a rotting carcass”. The term “vulture fund” refers to a hedge fund that seeks to profit from by buying up the distressed debt on secondary markets  at a below par price and then trying to recover ten times the purchase price, through suing them legally in European and American courts. These hedge funds are usually secretive and operate from offshore tax havens like the Cayman Islands. The ownership of these hedge funds are not divulged in most of the cases and are owned by billionaires from America and other countries, vulture funds target cheap debt of poor countries or countries under financial distress. These funds track the debt relief process, buy debt of nations about to get debt relief and then sue the country after it has received a windfall of resources thanks to debt cancellation.

Champerty

The vulture funds grind down   poor countries in cycles of litigation, commonly known as “champerty”, the litigation is usually protracted with many law suits taking three to ten years to settle. These vulture funds have won most of their law suits with a success rate of 72 percent, yielding more than $ 1 billion. The IMF reported that in some cases the claims by vulture funds constitute as much as 12 to 13 percent of a nation’s GDP, the litigation costs are a huge burden for the countries already under huge debt, causing inequitable sharing burden among creditors. Some of the countries affected by these wealthy hedge funds are Congo, Cameroon, Angola, Liberia, Tanzania and Uganda.

“Parri Passu”

One of the primary reasons why vulture funds are successful is because courts have been willing to enforce their right to collect their full value of debt. One of the most successful arguments is the inclusion of a clause called “parri passu” in many sovereign debt agreements; it is a common agreement between joint lenders under which, in the event of a shortfall, they agree to share whatever is available.

For example: when company “Y” goes into dissolution, the assets over which the charge has been created will be distributed in the proportion to the creditor’s respective holdings. Therefore, if the Bank “X” has tendered a loan facility of 60 million and another creditor “Z” has tendered 40 million, the recovery after selling assets of company “Y” to which parri passu charge has been attached, shall be distributed in the ratio of 6:4 among “X” and “Z”. The parri passu charge will have a huge effect on the countries due to two reasons:

1. Creditors can collect the full value of the debt
2. Restructuring which is a vital process for debt ridden countries will be stopped by the courts under the “parri passu” argument.

France files “Amicus Brief” 

A state run agency in Argentina reported last week that France submitted an ‘amicus brief ” to the court in favour of June 24^th petition before the Supreme Court. Argentina is asking the Supreme Court to ‘void’ the second U.S. Circuit Court of Appeals ruling last October that issued orders to the South American country to pay more than $ 1.3 billion to hedge funds that are suing for full repayment on defaulted bonds. The holdout creditors are demanding 100 cents on the dollar in a case that will end up before the U.S. Supreme Court, if the court accepts Argentina’s petition to hear the case.

Amicus Brief is a document that is filed in a court by a party who is not directly related to the case under consideration, States and Governments may also step in if they believe a case may impact them.

The post Vulture Funds Threat to HIPC appeared first on International Finance.