High frequency momentum trading with cryptocurrencies

Over the past few years, cryptocurrencies have increasingly been discussed as alternatives to traditional fiat currencies. These digital currencies have garnered significant interest from investment banks and portfolio managers as a potential option to diversify the financial risk from investing in other assets. This interest has also extended to the general public who have seen cryptocurrencies as a way of making a quick profit. This paper provides a first insight into the applicability of high frequency momentum trading strategies for cryptocurrencies. We implemented two variations of a signal-based momentum trading strategy: (i) a time series method; (ii) a cross sectional method. These strategies were tested on a selection of seven of the largest cryptocurrencies ranked by market capitalization. The results show that there exists potential for the momentum strategy to be used successfully for cryptocurrency trading in a high frequency setting. A comparison with a passive portfolio strategy is proposed, which shows abnormal returns when compared with the momentum strategies. Furthermore, the robustness of our results are checked through the application of the momentum strategies other sample periods. We also compare the performances of the signal-based momentum strategies with returns-based versions of the strategies. It is shown that the signal-based strategy outperforms the returns-based strategy. However, there appears to be no single parameterization of the signal-based strategies that can generate the greatest cumulative return over all sample periods.     

Time series momentum and moving average trading rules

We compare and contrast time series momentum (TSMOM) and moving average (MA) trading rules so as to better understand the sources of their profitability. These rules are closely related; however, there are important differences. TSMOM signals occur at points that coincide with a MA direction change, whereas MA buy (sell) signals only require price to move above (below) a MA. Our empirical results show MA rules frequently give earlier signals leading to meaningful return gains. Both rules perform best outside of large stock series which may explain the puzzle of their popularity with investors, yet lack of supportive evidence in academic studies.     

Time series momentum trading strategy and autocorrelation amplification

This paper investigates why general Moving Average (MA) trading rules are widely used by technical analysts and others. We assume general stationary processes for prices and we derive the autocorrelation function for an MA trading rule. Based on our results, we conjecture that autocorrelation amplification is one of the reasons why such trading rules are popular. Using simulated results, we show that the MA rule may be popular because it can identify price momentum and is a simple way of assessing and exploiting the price autocorrelation structure without necessarily knowing its precise structure. This paper then, provides empirical evidence of autocorrelation amplification using 15-year daily price data for 11 major international stock indices.     

Continuous overreaction,insiders trading activities and momentum strategies

The paper investigates the influence and explanatory power of aggregate insiders trading activities on momentum trading strategies. We find that insiders trading activities can predict cross-sectional returns and can strengthen the naı̈ve momentum effects. The risk factors such as size and BM cannot explain the strong momentum effects in our refined momentum strategies. We interpret our findings as that the continuous overreaction causes the mediate term momentum effects and over pricing. In the long term, these overly priced stocks will be corrected with passing time. The correction of over pricing causes long-term reversals.     

Informed trading,information uncertainty,and price momentum

In this paper we investigate the effects of informed trading (PIN) and information uncertainty in determining price momentum. We find that trading strategies based on buying high-uncertainty good-news stocks and shorting high-uncertainty bad-news stocks work well when limited to high-PIN stocks, while stocks with low-PIN do not exhibit price continuations, even when the uncertainty level of those stocks is high. In contrast, momentum returns are always significant for high-PIN stocks, irrespective of information uncertainty. Overall, we show that the informed trading effect is both independent of and stronger than that of information uncertainty in determining price momentum.     

Futures trading volume as a determinant of prices in different momentum phases

Recent studies contend that trading volume has predictive power for ex ante stock prices, particularly small stocks that do not react quickly to macroeconomic information. This study postulates that a significant amount of macro-information that flows on to stock markets is derived from derivative markets. We examine the impact of short-term futures trading volume and prices on cash stock prices using a case study of 15-min data from the Australian stock index futures market which reports actual trading volume. After applying vector error correction modelling (VECM), variance decomposition and impulse functions, we conclude that futures prices provide a short-term information lead to stock prices that dominates trading volume effects. We also observe asymmetric changes in the impact of trading volume between bull and bear price momentum phases and after large trading volume shocks. These results suggest that, in future, studies on trading volume should control for the cross-correlation impact from derivative prices and the differential impact of trading phases.     

CDS trading and nonrelationship lending dynamics

Review of Accounting Studies - We investigate how credit default swaps (CDSs) affect lenders’ incentives to initiate new lending relationships. We predict that CDSs reduce adverse selection...     

Combining mean reversion and momentum trading strategies in foreign exchange markets

The literature on equity markets documents the existence of mean reversion and momentum phenomena. Researchers in foreign exchange markets find that foreign exchange rates also display behaviors akin to momentum and mean reversion. This paper implements a trading strategy combining mean reversion and momentum in foreign exchange markets. The strategy was originally designed for equity markets, but it also generates abnormal returns when applied to uncovered interest parity deviations for five countries. I find that the pattern for the positions thus created in the foreign exchange markets is qualitatively similar to that found in the equity markets. Quantitatively, this strategy performs better in foreign exchange markets than in equity markets. Also, it outperforms traditional foreign exchange trading strategies, such as carry trades and moving average rules.     

Price dynamics and speculative trading in bitcoin

Few innovations in the money markets have brought more attention by regulators and policy makers than the digital currency Bitcoin. However, few studies in the literature have examined the price dynamics of Bitcoin. Besides providing an exploratory glace at the value and volatility of the Bitcoin across time, we also test whether the unusual level of Bitcoin’s volatility is attributable to speculative trading. Results in this study do not find that, during 2013, speculative trading contributed to the unprecedented rise and subsequent crash in Bitcoin’s value nor do we find that speculative trading is directly associated with Bitcoin’s unusual level of volatility.     

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.     

Informed momentum trading versus uninformed “naive” investors strategies

We construct a zero net-worth uninformed “naive investor” who uses a random portfolio allocation strategy. We then compare the returns of the momentum strategist to the return distribution of naive investors. For this purpose we reward momentum profits relative to the return percentiles of the naive investors with scores that are symmetric around the median. The score function thus constructed is invariant and robust to risk factor models. We find that the average scores of the momentum strategies are close to zero (the score of the median) and statistically insignificant over the sample period between 1926 and 2005, various sub-sample periods including the periods examined in [Jegadeesh and Titman, 1993] and [Jegadeesh and Titman, 2001]. The findings are robust with respect to sampling or period-specific effects, tightened score intervals, and the imposition of maximum-weight restrictions on the naive strategies to mitigate market friction considerations.     

The dynamics of dealer markets and trading costs

In this study we examine the temporal dynamics of dealer market share and their ramification for competition and trading costs using a large sample of NASDAQ securities. Our results show that although the total market share of the top five dealers is relatively stable over time, there is significant monthly variation in the composition of the top five dealers. We show that market share turbulence among top dealers is another form of competition that narrows bid–ask spreads, especially for stocks with less competitive market structure.     

Optimal trading strategy and supply/demand dynamics

In this paper, we study how the intertemporal supply/demand of a security affects trading strategy. We develop a general framework for a limit order book market to capture the dynamics of supply/demand. We show that the optimal strategy to execute an order does not depend on the static properties of supply/demand such as bid–ask spread and market depth, it depends on their dynamic properties such as resilience: the speed at which supply/demand recovers to its steady state after a trade. In general, the optimal strategy is quite complex, mixing large and small trades, and can substantially lower execution cost. Large trades remove the existing liquidity to attract new liquidity, while small trades allow the trader to further absorb any incoming liquidity flow.     

A computing bias in estimating the probability of informed trading

This study identifies a factor that leads to a bias in estimating the probability of informed trading (PIN), a widely-used microstructure measure. It is shown that, along with the numerical maximization of the likelihood function for PIN, the floating-point exception (i.e., overflow or underflow) may eliminate feasible solutions to the actual parameters in the optimization problem. Approximately 44% of PIN estimates for recent stock market data may have been subject to a downward bias that is more pronounced for active stocks than for inactive stocks. This study develops a remedy to mitigate the resulting bias.     

A momentum trading strategy based on the low frequency component of the exchange rate

In this paper, we develop a momentum trading strategy based on the low frequency trend component of the spot exchange rate. Using kernel regression and the high-pass filter of Hodrick and Prescott [Hodrick, R., Prescott, E., 1997. Post-war US business cycles: An empirical investigation. Journal of Money, Credit and Banking 29, 1–16], we recover the non-linear trend in the monthly exchange rate and use short-term momentum in this to generate buy and sell signals. The low frequency momentum trading strategy offers greater directional accuracy, higher returns and Sharpe ratios, lower maximum drawdown and less frequent trading than traditional moving average rules. Moreover, unlike traditional moving average rules, the performance of the low frequency momentum trading strategy is relatively robust across different time periods. The low frequency momentum trading strategy is also robust to the choice of smoothing parameter (in the case of the HP filter) and the distribution and bandwidth parameter (in the case of kernel regression) over a wide range of values.     

The relevance of information and trading costs in explaining momentum profits: Evidence from optioned and non-optioned stocks

Considerable evidence from many countries suggests momentum strategies generate profits. These have been difficult to rationalise and evidence on the sources of such profitability is inconclusive. We utilise a sample of optioned stocks, characterised by high liquidity, high market capitalisation and fewer short sales constraints and compare results with control samples of non optioned stocks chosen on the basis of market value, turnover and bid–ask spread. The sample characteristics, and the fact that derivatives improve the impounding of information into prices, enable us to draw conclusions about the causes of momentum profits. While we find that short sales constraints are not the major driver of profitability and that most momentum profits disappear using two transactions costs measures of the bid–ask spread, one not previously used, the persistence of some momentum profits indicates that the market underreacts even to the most publicly available information.     

Contemporaneous ADR pricing: intraday dynamics during overlapping trading hours

We contribute to the literature by identifying and accurately measuring the drivers of American depositary receipt (ADR) returns contemporaneously across various global time zones. We consider ADRs as two inherently distinct asset classes – stocks and currencies – bundled into one. Throughout, we use a relatively refined, focused, and synchronized minute-by-minute data set on ADRs and all other variables. ADRs from all countries with regular trading hours that overlap with those of the US are considered individually and in clusters. We analyze the interplay of several factors that influence ADRs pricing patterns. Further, we investigate whether such patterns vary by currency, ADR, industry, and emerging/developed market classifications. Our findings indicate that synchronized returns on underlying shares comprise 68.5–74% of the explained returns in ADRs. The remaining 31.5–26% of returns are generated by movements in currency rates. These results are robust across the several models and estimation methods employed. Our findings also show persistent small price discrepancies between ADRs and dollar-adjusted underlying shares on a minute-by-minute basis, implying possible arbitrage opportunities. However, we conclude that trading and ADR conversion costs render such opportunities unattractive.     

Insider trading with correlation between liquidity trading and a public signal

We analyse a Kyle-type continuous-time market model in which liquidity trading is correlated with a noisy public signal that is released continuously. We show that, in contrast to the previous literature, Kyle's λ, the price sensitivity to the order flow, can even be non-monotonic, depending on the correlation structure. We also show that the introduction of an additional public signal does not necessarily improve the informational efficiency of the market, depending on the correlation.     

The dynamics of individual and institutional trading on the Shanghai Stock Exchange

We investigate the daily dynamic relation between returns and institutional and individual trades in the emerging Chinese stock market. Consistent with the hypotheses of trend-chasing and attention-grabbing trading, we find that the response of individual trading to return shocks is much stronger than that of institutional trading, and individuals are net buyers following return shocks. Second, we find that past individual buys and sells have predictive power, whereas past institutional buys and sells have predictive power for market returns in longer horizons. However, both institutional and individual trading activities are more strongly related to past trades than past returns, and individual trading is also influenced by institutional trading. Moreover, we find that institutional trading in the largest quintile leads the trading in the smallest quintile, but no such lead–lag relation is found for individual trades. Finally, we find that the average cumulative abnormal trading volume of individuals is much larger than that of institutions around the firms' earnings announcement, suggesting that less-informed individual investors are more heavily influenced by firm-specific information disclosures and attention-grabbing events.     

Entry, exit and trading profits: A look at the trading strategies of a proprietary trading team

We examine the behavior of a 15 strong proprietary stock trading team and show how consistent intraday trading profits were generated. The team, who worked for a large US direct access trading firm, executed over 96 thousand trades in 3 months in 2000. Profitable intraday trading occurred in an anonymous dealer capacity, on both long and short positions, especially when volume and price volatility were higher. The traders rapidly entered long (short) positions when the number of dealers and size become greater on the bid (offer) side of the spread. Profits were taken early against the trend.