Private Equity and Corporate Governance. Thought of the Day 109.0

The two historical models of corporate ownership are (1) dispersed public ownership across many shareholders and (2) family-owned or closely held. Private equity ownership is a hybrid between these two models.

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The main advantages of public ownership include giving a company the widest possible access to capital and, for start-up companies, more credibility with suppliers and customers. The key disadvantages are that a public listing of stock brings constant scrutiny by regulators and the media, incurs significant costs (listing, legal and other regulatory compliance costs), and creates a significant focus on short-term financial results from a dispersed base of shareholders (many of whom are not well informed). Most investors in public companies have limited ability to influence a company’s decision making because ownership is so dispersed. As a result, if a company performs poorly, these investors are inclined to sell shares instead of attempting to engage with management through the infrequent opportunities to vote on important corporate decisions. This unengaged oversight opens the possibility of managers potentially acting in ways that are contrary to the interests of shareholders.

Family-owned or closely held companies avoid regulatory and public scrutiny. The owners also have a direct say in the governance of the company, minimizing potential conflicts of interest between owners and managers. However, the funding options for these private companies are mainly limited to bank loans and other private debt financing. Raising equity capital through the private placement market is a cumbersome process that often results in a poor outcome.

Private equity firms offer a hybrid model that is sometimes more advantageous for companies that are uncomfortable with both the family-owned/closely held and public ownership models. Changes in corporate governance are generally a key driver of success for private equity investments. Private equity firms usually bring a fresh culture into corporate boards and often incentivize executives in a way that would usually not be possible in a public company. A private equity fund has a vital self-interest to improve management quality and firm performance because its investment track record is the key to raising new funds in the future. In large public companies there is often the possibility of “cross-subsidization” of less successful parts of a corporation, but this suboptimal behavior is usually not found in companies owned by private equity firms. As a result, private equity-owned companies are more likely to expose and reconfigure or sell suboptimal business segments, compared to large public companies. Companies owned by private equity firms avoid public scrutiny and quarterly earnings pressures. Because private equity funds typically have an investment horizon that is longer than the typical mutual fund or other public investor, portfolio companies can focus on longer-term restructuring and investments.

Private equity owners are fully enfranchised in all key management decisions because they appoint their partners as nonexecutive directors to the company’s board, and some- times bring in their own managers to run the company. As a result, they have strong financial incentives to maximize shareholder value. Since the managers of the company are also required to invest in the company’s equity alongside the private equity firm, they have similarly strong incentives to create long-term shareholder value. However, the significant leverage that is brought into a private equity portfolio company’s capital structure puts pressure on management to operate virtually error free. As a result, if major, unanticipated dislocations occur in the market, there is a higher probability of bankruptcy compared to either the family-owned/closely held or public company model, which includes less leverage. The high level of leverage that is often connected with private equity acquisition is not free from controversy. While it is generally agreed that debt has a disciplining effect on management and keeps them from “empire building,” it does not improve the competitive position of a firm and is often not sustainable. Limited partners demand more from private equity managers than merely buying companies based on the use of leverage. In particular, investors expect private equity managers to take an active role in corporate governance to create incremental value.

Private equity funds create competitive pressures on companies that want to avoid being acquired. CEOs and boards of public companies have been forced to review their performance and take steps to improve. In addition, they have focused more on antitakeover strategies. Many companies have initiated large share repurchase programs as a vehicle for increasing earnings per share (sometimes using new debt to finance repurchases). This effort is designed, in part, to make a potential takeover more expensive and therefore less likely. Companies consider adding debt to their balance sheet in order to reduce the overall cost of capital and achieve higher returns on equity. This strategy is sometimes pursued as a direct response to the potential for a private equity takeover. However, increasing leverage runs the risk of lower credit ratings on debt, which increases the cost of debt capital and reduces the margin for error. Although some managers are able to manage a more leveraged balance sheet, others are ill equipped, which can result in a reduction in shareholder value through mismanagement.

Malignant Acceleration in Tech-Finance. Some Further Rumination on Regulations. Thought of the Day 72.1

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Regardless of the positive effects of HFT that offers, such as reduced spreads, higher liquidity, and faster price discovery, its negative side is mostly what has caught people’s attention. Several notorious market failures and accidents in recent years all seem to be related to HFT practices. They showed how much risk HFT can involve and how huge the damage can be.

HFT heavily depends on the reliability of the trading algorithms that generate, route, and execute orders. High-frequency traders thus must ensure that these algorithms have been tested completely and thoroughly before they are deployed into the live systems of the financial markets. Any improperly-tested, or prematurely-released algorithms may cause losses to both investors and the exchanges. Several examples demonstrate the extent of the ever-present vulnerabilities.

In August 2012, the Knight Capital Group implemented a new liquidity testing software routine into its trading system, which was running live on the NYSE. The system started making bizarre trading decisions, quadrupling the price of one company, Wizzard Software, as well as bidding-up the price of much larger entities, such as General Electric. Within 45 minutes, the company lost USD 440 million. After this event and the weakening of Knight Capital’s capital base, it agreed to merge with another algorithmic trading firm, Getco, which is the biggest HFT firm in the U.S. today. This example emphasizes the importance of implementing precautions to ensure their algorithms are not mistakenly used.

Another example is Everbright Securities in China. In 2013, state-owned brokerage firm, Everbright Securities Co., sent more than 26,000 mistaken buy orders to the Shanghai Stock Exchange (SSE of RMB 23.4 billion (USD 3.82 billion), pushing its benchmark index up 6 % in two minutes. This resulted in a trading loss of approximately RMB 194 million (USD 31.7 million). In a follow-up evaluative study, the China Securities Regulatory Commission (CSRC) found that there were significant flaws in Everbright’s information and risk management systems.

The damage caused by HFT errors is not limited to specific trading firms themselves, but also may involve stock exchanges and the stability of the related financial market. On Friday, May 18, 2012, the social network giant, Facebook’s stock was issued on the NASDAQ exchange. This was the most anticipated initial public offering (IPO) in its history. However, technology problems with the opening made a mess of the IPO. It attracted HFT traders, and very large order flows were expected, and before the IPO, NASDAQ was confident in its ability to deal with the high volume of orders.

But when the deluge of orders to buy, sell and cancel trades came, NASDAQ’s trading software began to fail under the strain. This resulted in a 30-minute delay on NASDAQ’s side, and a 17-second blackout for all stock trading at the exchange, causing further panic. Scrutiny of the problems immediately led to fines for the exchange and accusations that HFT traders bore some responsibility too. Problems persisted after opening, with many customer orders from institutional and retail buyers unfilled for hours or never filled at all, while others ended up buying more shares than they had intended. This incredible gaffe, which some estimates say cost traders USD 100 million, eclipsed NASDAQ’s achievement in getting Facebook’s initial IPO, the third largest IPO in U.S. history. This incident has been estimated to have cost investors USD 100 million.

Another instance occurred on May 6, 2010, when U.S. financial markets were surprised by what has been referred to ever since as the “Flash Crash” Within less than 30 minutes, the main U.S. stock markets experienced the single largest price declines within a day, with a decline of more than 5 % for many U.S.-based equity products. In addition, the Dow Jones Industrial Average (DJIA), at its lowest point that day, fell by nearly 1,000 points, although it was followed by a rapid rebound. This brief period of extreme intraday volatility demonstrated the weakness of the structure and stability of U.S. financial markets, as well as the opportunities for volatility-focused HFT traders. Although a subsequent investigation by the SEC cleared high-frequency traders of directly having caused the Flash Crash, they were still blamed for exaggerating market volatility, withdrawing liquidity for many U.S.-based equities (FLASH BOYS).

Since the mid-2000s, the average trade size in the U.S. stock market had plummeted, the markets had fragmented, and the gap in time between the public view of the markets and the view of high-frequency traders had widened. The rise of high-frequency trading had been accompanied also by a rise in stock market volatility – over and above the turmoil caused by the 2008 financial crisis. The price volatility within each trading day in the U.S. stock market between 2010 and 2013 was nearly 40 percent higher than the volatility between 2004 and 2006, for instance. There were days in 2011 in which volatility was higher than in the most volatile days of the dot-com bubble. Although these different incidents have different causes, the effects were similar and some common conclusions can be drawn. The presence of algorithmic trading and HFT in the financial markets exacerbates the adverse impacts of trading-related mistakes. It may lead to extremely higher market volatility and surprises about suddenly-diminished liquidity. This raises concerns about the stability and health of the financial markets for regulators. With the continuous and fast development of HFT, larger and larger shares of equity trades were created in the U.S. financial markets. Also, there was mounting evidence of disturbed market stability and caused significant financial losses due to HFT-related errors. This led the regulators to increase their attention and effort to provide the exchanges and traders with guidance on HFT practices They also expressed concerns about high-frequency traders extracting profit at the costs of traditional investors and even manipulating the market. For instance, high-frequency traders can generate a large amount of orders within microseconds to exacerbate a trend. Other types of misconduct include: ping orders, which is using some orders to detect other hidden orders; and quote stuffing, which is issuing a large number of orders to create uncertainty in the market. HFT creates room for these kinds of market abuses, and its blazing speed and huge trade volumes make their detection difficult for regulators. Regulators have taken steps to increase their regulatory authority over HFT activities. Some of the problems that arose in the mid-2000s led to regulatory hearings in the United States Senate on dark pools, flash orders and HFT practices. Another example occurred after the Facebook IPO problem. This led the SEC to call for a limit up-limit down mechanism at the exchanges to prevent trades in individual securities from occurring outside of a specified price range so that market volatility will be under better control. These regulatory actions put stricter requirements on HFT practices, aiming to minimize the market disturbance when many fast trading orders occur within a day.

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Momentum of Accelerated Capital. Note Quote.

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Distinct types of high frequency trading firms include independent proprietary firms, which use private funds and specific strategies which remain secretive, and may act as market makers generating automatic buy and sell orders continuously throughout the day. Broker-dealer proprietary desks are part of traditional broker-dealer firms but are not related to their client business, and are operated by the largest investment banks. Thirdly hedge funds focus on complex statistical arbitrage, taking advantage of pricing inefficiencies between asset classes and securities.

Today strategies using algorithmic trading and High Frequency Trading play a central role on financial exchanges, alternative markets, and banks‘ internalized (over-the-counter) dealings:

High frequency traders typically act in a proprietary capacity, making use of a number of strategies and generating a very large number of trades every single day. They leverage technology and algorithms from end-to-end of the investment chain – from market data analysis and the operation of a specific trading strategy to the generation, routing, and execution of orders and trades. What differentiates HFT from algorithmic trading is the high frequency turnover of positions as well as its implicit reliance on ultra-low latency connection and speed of the system.

The use of algorithms in computerised exchange trading has experienced a long evolution with the increasing digitalisation of exchanges:

Over time, algorithms have continuously evolved: while initial first-generation algorithms – fairly simple in their goals and logic – were pure trade execution algos, second-generation algorithms – strategy implementation algos – have become much more sophisticated and are typically used to produce own trading signals which are then executed by trade execution algos. Third-generation algorithms include intelligent logic that learns from market activity and adjusts the trading strategy of the order based on what the algorithm perceives is happening in the market. HFT is not a strategy per se, but rather a technologically more advanced method of implementing particular trading strategies. The objective of HFT strategies is to seek to benefit from market liquidity imbalances or other short-term pricing inefficiencies.

While algorithms are employed by most traders in contemporary markets, the intense focus on speed and the momentary holding periods are the unique practices of the high frequency traders. As the defence of high frequency trading is built around the principles that it increases liquidity, narrows spreads, and improves market efficiency, the high number of trades made by HFT traders results in greater liquidity in the market. Algorithmic trading has resulted in the prices of securities being updated more quickly with more competitive bid-ask prices, and narrowing spreads. Finally HFT enables prices to reflect information more quickly and accurately, ensuring accurate pricing at smaller time intervals. But there are critical differences between high frequency traders and traditional market makers:

  1. HFT do not have an affirmative market making obligation, that is they are not obliged to provide liquidity by constantly displaying two sides quotes, which may translate into a lack of liquidity during volatile conditions.
  2. HFT contribute little market depth due to the marginal size of their quotes, which may result in larger orders having to transact with many small orders, and this may impact on overall transaction costs.
  3. HFT quotes are barely accessible due to the extremely short duration for which the liquidity is available when orders are cancelled within milliseconds.

Besides the shallowness of the HFT contribution to liquidity, are the real fears of how HFT can compound and magnify risk by the rapidity of its actions:

There is evidence that high-frequency algorithmic trading also has some positive benefits for investors by narrowing spreads – the difference between the price at which a buyer is willing to purchase a financial instrument and the price at which a seller is willing to sell it – and by increasing liquidity at each decimal point. However, a major issue for regulators and policymakers is the extent to which high-frequency trading, unfiltered sponsored access, and co-location amplify risks, including systemic risk, by increasing the speed at which trading errors or fraudulent trades can occur.

Although there have always been occasional trading errors and episodic volatility spikes in markets, the speed, automation and interconnectedness of today‘s markets create a different scale of risk. These risks demand that exchanges and market participants employ effective quality management systems and sophisticated risk mitigation controls adapted to these new dynamics in order to protect against potential threats to market stability arising from technology malfunctions or episodic illiquidity. However, there are more deliberate aspects of HFT strategies which may present serious problems for market structure and functioning, and where conduct may be illegal, for example in order anticipation seeks to ascertain the existence of large buyers or sellers in the marketplace and then to trade ahead of those buyers and sellers in anticipation that their large orders will move market prices. A momentum strategy involves initiating a series of orders and trades in an attempt to ignite a rapid price move. HFT strategies can resemble traditional forms of market manipulation that violate the Exchange Act:

  1. Spoofing and layering occurs when traders create a false appearance of market activity by entering multiple non-bona fide orders on one side of the market at increasing or decreasing prices in order to induce others to buy or sell the stock at a price altered by the bogus orders.
  2. Painting the tape involves placing successive small amount of buy orders at increasing prices in order to stimulate increased demand.

  3. Quote Stuffing and price fade are additional HFT dubious practices: quote stuffing is a practice that floods the market with huge numbers of orders and cancellations in rapid succession which may generate buying or selling interest, or compromise the trading position of other market participants. Order or price fade involves the rapid cancellation of orders in response to other trades.

The World Federation of Exchanges insists: ― Exchanges are committed to protecting market stability and promoting orderly markets, and understand that a robust and resilient risk control framework adapted to today‘s high speed markets, is a cornerstone of enhancing investor confidence. However this robust and resilient risk control framework‘ seems lacking, including in the dark pools now established for trading that were initially proposed as safer than the open market.

Top-down Causation in Financial Markets. Note Quote.

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Regulators attempt to act on a financial market based on the intelligent and reasonable formulation of rules. For example, changing the market micro-structure at the lowest level in the hierarchy, can change the way that asset prices assimilate changes in information variables Zk,t or θi,m,t. Similarly, changes in accounting rules could change the meaning and behaviour of bottom-up information variables θi,m,t and changes in economic policy and policy implementation can change the meaning of top-down information variables Zk,t and influence shared risk factors rp,t.

In hierarchical analysis, theories and plans may be embodied in a symbolic system to build effective and robust models to be used for detecting deeper dependencies and emergent phenomena. Mechanisms for the transmission of information and asymmetric information information have impacts on market quality. Thus, Regulators can impact the activity and success of all the other actors, either directly or indirectly through knock-on effects. Examples include the following: Investor behaviour could change the goal selection of Traders; change in the latter could in turn impact variables coupled to Traders activity in such a way that Profiteers are able to benefit from change in liquidity or use leverage as a mean to achieve profit targets and overcome noise.

Idealistically, Regulators may aim for increasing productivity, managing inflation, reducing unemployment and eliminating malfeasance. However, the circumvention of rules, usually in the name of innovation or by claims of greater insight on optimality, is as much part of a complex system in which participants can respond to rules. Tax arbitrages are examples of actions which manipulate reporting to reduce levies paid to a profit- facilitating system. In regulatory arbitrage, rules may be followed technically, but nevertheless use relevant new information which has not been accounted for in system rules. Such activities are consistent with goals of profiteering but are not necessarily in agreement with longer term optimality of reliable and fair markets.

Rulers, i.e. agencies which control populations more generally, also impact markets and economies. Examples of top-down causation here include segregation of workers and differential assignment of economic rights to market participants, as in the evolution of local miners’ rights in the late 1800’s in South Africa and the national Native Land act of 1913 in South Africa, international agreements such as the Bretton Woods system, the Marshall plan of 1948, the lifting of the gold standard in 1973 and the regulation of capital allocations and capital flows between individual and aggregated participants. Ideas on target-based goal selection are already in circulation in the literature on applications of viability theory and stochastic control in economics. Such approaches provide alternatives to the Laplacian ideal of attaining perfect prediction by offering analysable future expectations to regulators and rulers.