Multi-market trading and arbitrage

We measure arbitrage opportunities by comparing the intraday prices and quotes of American Depositary Receipts (ADRs) and other types of cross-listed shares in U.S. markets with synchronous prices of their home-market shares on a currency-adjusted basis for a sample of 506 U.S. cross-listed stocks from 35 different countries. Deviations from price parity average an economically small 4.9 basis points, but they are volatile and can reach large extremes. Price parity deviations and their daily changes are positively related to proxies for holding costs that can impede arbitrage, even after controlling for transactions costs and foreign investment restrictions.     

Informed arbitrage with speculative noise trading

We consider speculative noise trading when some naïve speculators trade on noise as if it were information [Black, F., 1986. Noise. Journal of Finance 41, 529–543]. We examine the optimal trading strategy of an informed investor who faces such naïve speculators in the market. We find that the informed investor trades aggressively on her information and takes large, opposite positions against the naïve speculators. The trading volume is thereby drastically magnified. While such speculative noise trading enhances liquidity, it makes prices less efficient. The overall dynamic patterns that emerge from our model are most consistent with the evidence for interday variations in volume, volatility, and transaction costs.     

Jai Alai arbitrage strategies

This paper presents arbitrage and risk arbitrage betting strategies for Team Jai Alai. This game is the setting for the analysis and most results generalize to other sports betting situations and some financial market applications. The arbitrage conditions are utility free while the risk arbitrage wagers are constructed according to the Kelly criterion/capital growth theory that maximizes asymptotically long-run wealth almost surely.     

The box spread arbitrage conditions: theory, tests, and investment strategies

This paper develops and tests arbitrage bounds for a combinationof two option spread positions known as a box spread. This strategyinvolves the simultaneous use of four options and creates aposition that is equivalent to riskless lending. The no-arbitrageconditions are compared to existing arbitrage bounds and aretested using Chicago Board Options Exchange data.     

A note on sufficient conditions for no arbitrage

It is shown that the absence of call spread, butterfly spread and calendar spread arbitrages is sufficient to exclude all static arbitrages from a set of option price quotes across strikes and maturities on a single underlier.     

Tail-risk protection trading strategies

Starting from well-known empirical stylized facts of financial time series, we develop dynamic portfolio protection trading strategies based on econometric methods. As a criterion for riskiness, we consider the evolution of the value-at-risk spread from a GARCH model with normal innovations relative to a GARCH model with generalized innovations. These generalized innovations may for example follow a Student t, a generalized hyperbolic, an alpha-stable or a Generalized Pareto distribution (GPD). Our results indicate that the GPD distribution provides the strongest signals for avoiding tail risks. This is not surprising as the GPD distribution arises as a limit of tail behaviour in extreme value theory and therefore is especially suited to deal with tail risks. Out-of-sample backtests on 11 years of DAX futures data, indicate that the dynamic tail-risk protection strategy effectively reduces the tail risk while outperforming traditional portfolio protection strategies. The results are further validated by calculating the statistical significance of the results obtained using bootstrap methods. A number of robustness tests including application to other assets further underline the effectiveness of the strategy. Finally, by empirically testing for second-order stochastic dominance, we find that risk averse investors would be willing to pay a positive premium to move from a static buy-and-hold investment in the DAX future to the tail-risk protection strategy.     

Dynamic mode decomposition for financial trading strategies

We demonstrate the application of an algorithmic trading strategy based upon the recently developed dynamic mode decomposition on portfolios of financial data. The method is capable of characterizing complex dynamical systems, in this case financial market dynamics, in an equation-free manner by decomposing the state of the system into low-rank terms whose temporal coefficients in time are known. By extracting key temporal coherent structures (portfolios) in its sampling window, it provides a regression to a best fit linear dynamical system, allowing for a predictive assessment of the market dynamics and informing an investment strategy. The data-driven analytics capitalizes on stock market patterns, either real or perceived, to inform buy/sell/hold investment decisions. Critical to the method is an associated learning algorithm that optimizes the sampling and prediction windows of the algorithm by discovering trading hot-spots. The underlying mathematical structure of the algorithms is rooted in methods from nonlinear dynamical systems and shows that the decomposition is an effective mathematical tool for data-driven discovery of market patterns.     

A note on the relationship between high-frequency trading and latency arbitrage

We develop three artificial stock markets populated with two types of market participants — HFT scalpers and aggressive high frequency traders (HFTrs). We simulate real-life trading at the millisecond interval by applying Strongly Typed Genetic Programming (STGP) to real-time data from Cisco Systems, Intel and Microsoft. We observe that HFT scalpers are able to calculate NASDAQ NBBO (National Best Bid and Offer) at least 1.5 ms ahead of the NASDAQ SIP (Security Information Processor), resulting in a large number of latency arbitrage opportunities. We also demonstrate that market efficiency is negatively affected by the latency arbitrage activity of HFT scalpers, with no countervailing benefit in volatility or any other measured variable. To improve market quality, and eliminate the socially wasteful arms race for speed, we propose batch auctions in every 70 ms of trading.     

Mispricing of dual-class shares: Profit opportunities,arbitrage, and trading

This is the first paper to examine the microstructure of how mispricing is created and resolved. We study dual-class shares with equal cash flow rights and show that a simple trading strategy exploiting gaps between their prices appears to create abnormal profits after transactions costs. Trade and quote data show that investors shift their trading patterns to take advantage of gaps. Contrary to common perception, long–short arbitrage plays a minor part in eliminating gaps, and one-sided trades correct most of them. We also show that the more liquid share class is usually responsible for the price discrepancies.     

Return enhancement trading strategies for size based portfolios

Recent theoretical work suggests that definitions of market efficiency that allow for the possibility of time-varying risk-premia will generally lead to return sign predictability. Consistent with this theory, we show that a logit model based on the lagged value of the market risk premium is useful for successfully predicting the return sign for CRSP small decile portfolio returns, but not large ones. We additionally employ this model in market timing simulations of micro-cap mutual funds in which investment can actually be made. The results indicate that a market-timing strategy based on our return-sign forecasting model outperforms a buy-and-hold strategy for 13 of 14 micro-cap funds studied. On average, the buy-and-hold strategy produces an average compound return of 11.98% per annum versus an average of 16.60% for the market-timing strategy. Nevertheless, trading restrictions make the return-sign forecasting model more practical to employ by the micro-cap fund portfolio manager rather than the individual fund investor.

An anatomy of trading strategies

In this article we use a single unifying framework to analyzethe sources of profits to a wide spectrum of return-based tradingstrategies implemented in the literature. We show that lessthan 50% of the 120 strategies implemented in the article yieldstatistically significant profits and, unconditionally, momentumand contrarian strategies are equally likely to be successful.However, when we condition on the return horizon (short, medium,or long) of the strategy, or the time period during which itis implemented, two patterns emerge. A momentum strategy isusually profitable at the medium (3- to 12-months) horizon,while a contrarian strategy nets statistically significant profitsat long horizons, but only during the 1926-1947 subperiod. Moreimportantly, our results show that the cross-sectional variationin the mean returns of individual securities included in thesestrategies play an important role in their profitability. Thecross-sectional variation can potentially account for the profitabilityof momentum strategies and it is also responsible for attenuatingthe profits from price reversals to long-horizon contrarianstrategies.     

Is smarter better? A comparison of adaptive, and simple moving average trading strategies

This study examines the comparative performance of an Adaptive Moving Average (AMA) on the Australian All Ordinaries, Dow Jones Industrial Average, and Standard and Poor's 500 stock market indices. The theoretical advantage of the Adaptive Moving Average over fixed length Simple Moving Average (SMA) trading systems is its ability to automatically respond to changing market conditions dependant upon the level of volatility in the market. While the strategy is confirmed to have some market timing ability, the overall results show returns to the Adaptive Moving Average cannot compensate for the cost of trade therefore lending support for the use of a long run passive strategy.     

A simple and effective trading rule for individual investors

This study advocates the use of a simple trading strategy that is shown to outperform a passive buy and hold the market strategy. Based on prior studies that find long-term stock price responses to economic news, the strategy utilizes the announcement of discount rate changes to predict (and profit from) market movements. Statistical testing indicates that the strategy correctly predicts market movements. Furthermore, the results indicate that the proposed trading strategy produces higher risk adjusted returns than a buy and hold strategy. Thus individual investors may reasonably expect to profit by following this easy to implement trading strategy.     

Estimating the profits from trading strategies

Price improvement is the difference between the execution priceof an order and the quoted bid or ask when the order was submitted.We show that expected price improvement falls off dramaticallyas the size of the order approaches the quoted depth, and becomesnegative for larger orders. This is particularly important forsmall firms because the quoted depths are low. Using quotedspreads and depths and our estimate of expected price improvement,we show that trading strategies that attempt to exploit theweekly predictability of small-firm returns would be swampedby transaction costs.     

Long-run relations among equity indices under different market conditions: Implications on the implementation of statistical arbitrage strategies

Compared with previous research, the present work extends existing literature by considering long-run relations among major international stock market indices, under different market conditions, and the implications of these relations on the implementation of statistical arbitrage strategies. The examined data contain two bust phases interrupted by a mild bullish period. Employing cointegration analysis, reported results initially indicate that changes in market performance affect the stability of long-run relations, therefore suggesting that arbitrageurs should perform rebalancing among the examined indices when a change in a market trend is evident. Furthermore, extreme market performance harms the mean-reverting properties of a potential long-run relation while moderate market performance points to cointegration between a pair of indices. However, the absence of a stationary spread does not suggest the potential of abnormal returns realization, in the short-run, through exploitation of deviations from its mean value. The applicability of our results may be of importance to market participants since the cointegration approach has recently received considerable attention by hedge funds adopting statistical arbitrage strategies.     

Asymmetric return dynamics and technical trading strategies

We investigate the profitability of technical trading strategies based on an asymmetric reverting property of stock returns. We identify an asymmetry in return dynamics for daily returns on the S&P 500 index. Return dynamics evolve along a positive (negative) unconditional mean after a prior positive (negative) return. The trading strategies based on this asymmetry generate a positive return for buy signals, a negative return for sell signals, and a positive return for the spread between buy and sell signals. Our results imply that the observed asymmetry in return dynamics is the main source of profitability for the implied strategies, thereby corroborating arguments for the usefulness of technical trading strategies.     

Constant-collateral pyramiding trading strategies in futures markets

This paper introduces constant-collateral pyramiding trading strategies, which can be implemented in the futures markets. For these strategies, expressions are derived for effective constraints on the number of futures contracts in the trader’s portfolio and on the trader’s wealth. Implications of the results are drawn regarding the degree of pyramiding adopted by a subgroup of noise traders who underestimate the probability of receiving a margin call when they engage in positive feedback strategies. Suggestions are made regarding how market regulators can use margin requirements to encourage these traders to adopt less aggressive pyramiding strategies.     

S&P 500 trading strategies and stock betas

This paper shows that S&P 500 stock betas are overstatedand the non-S&P 500 stock betas are understated becauseof liquidity price effects caused by the S&P 500 tradingstrategies. The daily and weekly betas of stocks added to theS&P 500 index during 1985-1989 increase, on average, by0.211 and 0.130. The difference between monthly betas of otherwisesimilar S&P 500 and non-S&P 500 stocks also equals 0.125during this period. Some of these increases can be explainedby the reduced nonsynchroneity of S&P 500 stock prices,but the remaining increases are explained by the price pressureor excess volatility caused by the S&P 500 trading strategies.I estimate that the price pressures account for 8.5 percentof the total variance of daily returns of a value-weighted portfolioof NYSE/AMEX stocks. The negative own autocorrelations in S&P500 index returns and the negative cross autocorrelations betweenS&P 500 stock returns provide further evidence consistentwith the price pressure hypothesis.