Complex Trading Strategies Research Articles

Interpreting institutional investment activity as a markov process: A stock recommender

Institutional investors like hedge funds are particularly resilient in times of volatility, primarily due to their access to a slew of complex trading strategies and extensive research capabilities; both of which are generally outside the reach of common investors. Fortunately, the U.S. Securities and Exchange Commission has mandated that large institutional investors publicly disclose the positions held by them at the end of a quarter, within the following 45 days. We then ask the question “Given our access to snapshots of positions held by large investors, can we extract alpha by approximating their aggregate behaviour?” In this paper, we introduce a stock recommendation model driven by the aggregate behaviour of institutional investors. We interpret stocks as the states of a Markov chain. The corresponding state transition matrix is defined based on the aggregate behaviour of investors. By designing the transition matrix to hold for aperiodicity and irreducibility, the steady state distribution of this chain is treated as a ranked list of stocks. We build a long only, equally weighted portfolio of stocks to trade using these recommendations. We observe that the returns on this portfolio beat existing models in the literature, and standard hedge fund indices both in terms of annualised returns and Sharpe ratio.

Unleashing the algorithmic frontier: Navigating the impact of algo trading on investor portfolios

In the ever-evolving landscape of financial markets, algo trading has emerged as a powerful force, revolutionizing the way investors execute trades. With the speed and precision of computer algorithms, algo trading offers advantages such as faster trade execution, reduced errors, and access to complex trading strategies. However, its impact on investors is not without its challenges and risks. This case explores the profound influence of algo trading on investors and their portfolios. It delves into the opportunities and pitfalls associated with this technological advancement, shedding light on its implications for market liquidity, volatility, and investor behavior. It explores the potential for improved portfolio diversification and efficient risk management, while also addressing concerns about market manipulation and unfair advantages. Furthermore, the case study investigates the regulatory environment surrounding algo trading, highlighting the measures implemented to ensure fair and transparent markets. By presenting a nuanced understanding of the impact of algo trading on investor portfolios, this case study equips readers with the knowledge and insights necessary to navigate the algorithmic frontier. It encourages critical thinking and strategic decision-making, empowering investors to harness the potential of algo trading while managing its inherent risks.

News-Induced Dynamic Networks for Market Signaling: Understanding the Impact of News on Firm Equity Value

Public news provides rich information about firm operations and market dynamics. Learning about firm interactions from news is commonly done by human investors but has not been realized by automatic methods, leading to a research opportunity in market signaling via dynamic firm relations. This study proposes a new text-mining approach to extract cobenefit/counter-benefit networks based on firms’ mutual or conflicting interests in business events. It reveals that the extracted dynamic networks provide additional value in predicting firm equity value over current adopted supply chain and coindustry networks, after controlling for market activities and other traditional indicators from news, such as volume, sentiment, and comentions. In practice, such cobenefit/counter-benefit networks provide good buy and sell signals, which enrich known indicators and support more complex trading strategies in investment and portfolio management. The analysis and visualization of the extracted cobenefit/counter-benefit networks are also useful in understanding the structure of the economy and assessing firm/industry changes in a timelier fashion.

Peer-to-peer electricity trading in grid-connected residential communities with household distributed photovoltaic

With the rapid development of distributed electricity generation technologies such as solar photovoltaics, peer-to-peer (P2P) electricity trading is attracting incremental attention due to the pursuit of decentralized energy autonomy and further economic benefits. The study presented in this paper aims to gain a better understanding of the community P2P electricity trading, including influence of trading strategies, determinants of trading quantity and price, and further factors affecting improvement in community energy welfare. The market mechanism is determined based on the discriminatory continuous double auction, followed by simulations of the trading process in well-designed cases. Results show that P2P trading price and quantity depend on variation of the supply–demand ratio during each trading period, by which in the absence of complex trading strategies and learning abilities, profits are able to be effectively and fairly allocated among participants. For a given community, the total profits that could be obtained from P2P electricity trading are influenced by many factors, and the key is to make those “ineffective supply” available in the P2P market again, through active energy management solutions on all residents rather than focusing only on prosumers’ self-consumption in the past. The ensuing results make significant sense as regards better understanding both the benefits and limitations of P2P electricity trading in residential communities or other similar scenes, thus helping for guiding practical implementation.

The Use of Credit Default Swaps in Fund Tournaments

We use the financial crisis of 2007–2009 as a laboratory to examine the costs and benefits of teams versus single managers in asset management. We find that when a fund uses complex trading strategies involving the use of CDS team-managed funds outperform solo-managed funds. This may be due to the greater diversity of expertise, experience and skill of teams relative to single managers. During the financial crisis, however, the performance premium of teams becomes negative, which may be because of the slower decision times of teams, which are especially costly during times of rapidly changing market conditions.

Combining technical trading rules using particle swarm optimization

Technical trading rules have been utilized in the stock market to make profit for more than a century. However, only using a single trading rule may not be sufficient to predict the stock price trend accurately. Although some complex trading strategies combining various classes of trading rules have been proposed in the literature, they often pick only one rule for each class, which may lose valuable information from other rules in the same class. In this paper, a complex stock trading strategy, namely performance-based reward strategy (PRS), is proposed. PRS combines the two most popular classes of technical trading rules – moving average (MA) and trading range break-out (TRB). For both MA and TRB, PRS includes various combinations of the rule parameters to produce a universe of 140 component trading rules in all. Each component rule is assigned a starting weight, and a reward/penalty mechanism based on rules’ recent profit is proposed to update their weights over time. To determine the best parameter values of PRS, we employ an improved time variant particle swarm optimization (TVPSO) algorithm with the objective of maximizing the annual net profit generated by PRS. The experiments show that PRS outperforms all of the component rules in the testing period. To assess the significance of our trading results, we apply bootstrapping methodology to test three popular null models of stock return: the random walk, the AR(1) and the GARCH(1,1). The results show that PRS is not consistent with these null models and has good predictive ability.

Trading Relative Performance with Alpha Indexes

Relative performance is central to investment management, and yet relative performance securities do not trade directly. Complex trading strategies must be devised to capture relative gains. The authors introduce a suite of relative performance indexes and index derivatives that offer new and attractive payoff structures. They demonstrate a variety of ways in which these products can provide a more efficient and cost-effective means of realizing investment objectives than can traditional futures and option markets.

Pitfalls and perils of financial innovation: The use of CDS by corporate bond funds

Abstract We use the financial crisis of 2007–2009 as a laboratory to examine the costs and benefits of teams versus single managers in asset management. We find that when a fund uses complex trading strategies involving the use of CDS team-managed funds outperform solo-managed funds. This may be due to the greater diversity of expertise, experience and skill of teams relative to single managers. During the financial crisis, however, the performance premium of teams becomes negative, which may be because of the slower decision times of teams, which are especially costly during times of rapidly changing market conditions.

Dominating Randomness - Applications of State Contingent Stochastic Ordering Methods to the Clustering and Performance Measurement of Trading Strategies

The rise in popularity of benchmark free and complex trading strategies throughout the last decade has made available a large variety of risk and performance profiles. As a consequence, to account for their complex performance characteristics, a lot of effort has been devoted to classify and value the performance of these strategies by the alterations of previous- or innovative measures. However, as most measures are often still simple path- and context independent statistics, most often the information provided proves inadequate to separate performance characteristics - as evidenced by the latest crisis. This paper provides a methodology that integrates the clustering and performance measurement of trading strategies in a context and preference based environment. It decomposes preferred performance characteristics into fragments of context dependent behaviour for clustering purposes. It subsequently aggregates these fragments of performance characteristics into a performance measure. The methodology allows for consideration of path dependencies. Two applications, in the clustering of hedge fund styles and the ordering of alternative equity strategies are given. A further application in the statistical replication of trading strategies is highlighted.

Trading Relative Performance with Alpha Indexes

Relative performance is central to investment management and yet relative performance securities do not trade directly. Complex trading strategies must be devised to capture relative gains. This paper introduces a suite of relative performance indexes and index derivatives that offer new and attractive payoff structures. We illustrate a variety of ways in which the products can provide a more efficient and cost-effective means of realizing investment objectives than can traditional futures and options markets.

Algorithmic Trading in the Global FX Market

As the role of hedge funds in the foreign exchange market continues to grow, algorithmic trading and technology are shaping the future of the market. A White Paper released by FXall, the world9s leading online foreign exchange platform, examines how the technology solutions and services provided by foreign exchange Electronic Communications Networks (ECNs) are evolving to meet the needs of this growing and rapidly developing market. The rise of hedge funds and algorithmic trading in FX is a key trend identified by the Bank for International Settlements (BIS) in the preliminary results of its latest survey announced on 25 September. The BIS reported that the FX trading volumes of financial institutions grew by 30% compared with 11% for reporting dealers since its last survey in 2004. FXall9s White Paper, entitled ‘Algorithmic Trading In The Global FX Market: The Need For Speed, Transparency And Fairness‘, looks at the implications of this trend for the future of FX trading in terms of the ever more complex technological needs and increasing discernment of market participants in selecting trading venues. The article highlights how the foreign exchange market is following the trend that is now well advanced in the equities market, where the use of automated, complex trading strategies has helped fuel the rise of ECNs. Hedge funds are successfully applying algorithmic strategies to FX trading to generate alpha, using many of the same methods they have applied in the equities market and other asset classes. This trend has injected more liquidity into the FX market — the world9s largest financial market. The market is further evolving in 2007 as participants seek the optimum technology to implement their strategies, fuelling the growth of FX as an asset class in its own right. <b>TOPICS:</b>Exchanges/markets/clearinghouses, risk management, global

Hedge Funds, Financial Intermediation, and Systemic Risk

Hedge funds, with assets under management approaching an estimated $1.5 trillion in 2006, have become important players in the U.S. and global capital markets. These largely unregulated funds differ from other market participants in their use of a variety of complex trading strategies and instruments, in their liberal use of leverage, in their opacity to outsiders, and in their convex compensation structure. These differences can exacerbate potential market failures stemming from agency problems, externalities, and moral hazard. Counterparty credit risk management (CCRM) practices, used by financial institutions to assess credit risk and limit counterparty exposure, are the first line of defense against market disruptions with potential systemic consequences. This article examines how the unique nature of hedge funds may generate market failures that make counterparty credit risk for exposures to the funds intrinsically more difficult to manage, both for regulated institutions and for policymakers concerned with systemic risk. The authors acknowledge that various market failures, such as the events surrounding the 1998 collapse of hedge fund Long-Term Capital Management, may make CCRM imperfect. However, CCRM has improved significantly since then, and it remains the appropriate starting point for limiting the potential for hedge funds to generate systemic disruptions.

Reexamining the Profitability of Technical Analysis with Data Snooping Checks

In this article we reexamine the profitability of technical analysis using White’s reality check and Hansen’s SPA test that correct the data snooping bias. Compared to previous studies, we study a more complete “universe” of trading techniques, including not only simple rules but also complex trading strategies, and we test the profitability of these rules and strategies with four main indices. It is found that significantly profitable simple rules and complex trading strategies do exist in the data from relatively “young” markets (NASDAQ Composite and Russell 2000) but not in the data from relatively “mature” markets [Dow Jones Industrial Average (DJIA) and S&P 500]. Moreover, after taking transaction costs into account, we find that the best rules for NASDAQ Composite and Russell 2000 outperform the buy-and-hold strategy in most in- and out-of-sample periods. It is also found that complex trading strategies are able to improve on the profits of simple rules and may even generate significant profits from unprofitable simple rules.

Benefits and risks of alternative investment strategies

As a result of the complex trading strategies they implement, and the full flexibility they have with respect to their ability to use derivatives and trade in illiquid markets, hedge fund managers, even those following zero-beta non-directional strategies, are exposed to a variety of risk factors (volatility risk, liquidity risk, credit risk, etc) in a potentially complex manner. This paper argues that a proper understanding of hedge fund risk extends much beyond a straightforward measure of linear exposure to market risk, and provides a detailed analysis of how modern portfolio theory allows the presence of these rewarded sources of risk to be accounted for when assessing the performance of hedge fund managers. The contrasted exposures of hedge fund managers to a large number of risk factors poses serious challenges to the investor, as it requires the use of appropriate techniques dedicated to their measure and control. In contrast, it is argued that this is also the driving force behind the diversification benefits investors enjoy when investing in hedge funds. The main message can be summarised as follows: the benefits and risks or alternative investment strategies are two facets of the same coin.

Quantitative Forecasting Models and Active Diversification for International Bonds

Extensive empirical evidence documents relatively consistent if modest predictability in excess bond returns and excess currency returns. The theory that active investors9 ability to add value on a risk-adjusted basis is proportional to their forecasting skill motivates the authors to extend empirical forecasting models to new sorts of trades-curve steepness positioning, and cross-country spread trading. They test the performance of increasingly complex trading strategies between 1992 and 2002, from using single indicators to predict specific trades, to pooling indicators into one forecasting model for each trade, and then to diversify across several trades. The broad composites produce the best risk-adjusted performance. One can gain an edge from the limited forecastability of returns and magnify this edge through diversification across strategies.

Execution Costs and Their Intraday Variation in Futures Markets

We consider trading costs in the transparent, competitive open outcry markets of the Chicago Mercantile Exchange (CME), in which market makers have no affirmative obligation to trade. We document that while CME spreads are similar in magnitude to those in other markets, realized spreads are often negative. A plausible explanation is that CME market makers are able to employ more complex trading strategies than their equity market counterparts because they are not bound by affirmative obligation. The evidence suggests that market transparency and market maker obligations are important determinants of intraday variation in trading costs. Copyright 2001 by University of Chicago Press.

Futures-forward price differentials in the T-bill markets: An application of the arbitrage pricing theory

Futures contracts are very similar to forward contracts. Both are agreements to trade an asset at a specific time in the future for a predetermined price. However, they are different in two aspects. First, the requirement of marking-to-market of futures contracts results in the difference in the timing and the size of cash flows between futures and forward contracts. Daily settlement of gains and losses is made by futures traders, but forward traders make no settlement until a reverse position is taken or a delivery is made. When interest rates are stochastic,the marking-to-market requirement results in differential pricing of futures and forward contracts. Second, the differential microstructure between futures and cash markets (see Kane 1980) also results in divergence between futures and forward prices. T-Bill futures and forward price differentials have been documented in a number of studies. Capozza and Cornell (1979) compared futures rates on T-Bills with forward rates contained in the term structure of interest rates. Their empirical results showed the existence of a differential that increased monotonically with the time to maturity of futures contracts. Elton, Gruber, and Rentzler (1984) used intraday prices and complex trading strategies in the test of efficiency of T-Bill futures markets. Their results indicated that the T-Bill futures markets were not efficient. Kawaller and Koch (1984) and Vignola and Dale (1980) both found significant futures and forward rate differentials on T-Bills when forward rates were implied by a Treasury yield curve. However, the differential became much smaller when forward rates were implied by the cash-and-carry trading relationship between the cash and the futures markets.

Intra-Day Tests of the Efficiency of the Treasury Bill Futures Market

In this paper we use intra-day prices to examine the efficiency of the Treasury bill futures market. The use of intra-day data allows us to carefully match in time trades in the futures and cash markets. By matching trades and allowing delays between observing apparent inefficiencies and the trade, we are examining realistic trading strategies. We find profitable arbitrage, swaps and a preferred strategy for initial purchase. These profits do not depend on an assumption of an equilibrium model. Hence these tests are direct tests of efficiency rather than a joint test of efficiency and equilibrium. T HIS paper examines the efficiency of the Treasury bill futures market. This subject has been examined before by Capozza and Cornell (1979), Cornell (1981), Lang and Rasche (1978), Rendleman and Carabini (1979), Vignola and Dale (1979), Poole (1978), and Puglisi (1978). There are a number of reasons why this paper makes an important contribution to the literature. The first reason is that by using trade by trade futures prices we can make more realistic assumptions about the pnces at which trades could have taken place. This paper uses intra-day prices to examine efficiency. Prior studies at best used closing prices to examine apparent inefficiencies and assumed that transactions took place at these closing prices. There are two problems with this procedure. First, simultaneous transactions cannot actually occur at closing prices. There is almost an hour difference between the closing of the Chicago futures and New York spot markets. Thus when the closing spot market price is observed the futures market has been closed for about an hour. Closing prices could be used to judge apparent discrepancies. However, it would be necessary to execute trades at the open. Making decisions based on closing prices and then assuming trades can take place at these prices is an approximation which could significantly affect results. In addition, using only closing prices ignores any intra-day trades which could lead to profits. In this study we match in time intra-day prices in the spot and futures market. These matched prices are used to identify apparently profitable trading rules. We then assume trades take place at prices which are observed later in the day. This is a test of a trading rule which an investor could actually follow. As we discuss later in the paper, our results differ from prior studies. These differences are primarily due to using intra-day prices and carefully matching times so that information used to initiate trades would actually be available to the investor at the time of the trade. The second reason is that we examine more complex trading strategies than those which have been employed in other studies. The random walk literature provides a useful analogy. The original random walk studies simply correlated past and future prices or returns. Later studies examined filter rules and other more complicated trading rules. In this study we employ alternative trading rules of increased complexity to test for the existence of trading profits. Finally, we explicitly examine the consequence of T-bills futures contracts being futures rather than forward contracts. Futures contracts, unlike forward contracts, involve flows every settlement day. The intermediate cash flows on futures contracts can affect the profitability of any strategy. The only prior study that even considered this effect is the one by Rendleman and Carabini (1979). They used the theoretical model of Cox, Ingersoll and Ross (1981) to try to estimate whether the difference between forward and futures prices was likely to be large enough to affect the payoff from alternative trading strategies. This is a useful first attempt. However, ultimately the effect of daily settlement or marking-to-the-market has to be examined directly as part of trading strategies to see if it affects the outcome of these strategies. There are other problems with prior studies. These involve gross estimates of prices and averaging data in a way that can mask inefficiencies. Since these problems are discussed in some detail in Rendleman and Carabini, there is no reason to review them further here. Probably no area in finance has received more attention than the efficiency literature. The reader may well wonder why, even given the approximaReceived for publication August 23, 1982. Revision accepted for publication May 20, 1983. * New York University, New York University, and Baruch College, respectively. The authors would like to thank the Center for the Study of Futures Markets at Columbia Business School for financial support.