Soybean Crush Spread Arbitrage: Trading Strategies and Market Efficiency

2007 ◽  
Author(s):  
John B. Mitchell
Keyword(s):  
Market Efficiency ◽  
Trading Strategies ◽  
Arbitrage Trading

Analysis of the Effects of Security Breaches on Firm Value

2018 ◽  
Vol 19 (2) ◽  
pp. 1-25
Author(s):  
Stoyu Ivanov
Keyword(s):  
Market Efficiency ◽  
Market Microstructure ◽  
Hedge Funds ◽  
High Frequency ◽  
Firm Value ◽  
Trading Strategies ◽  
Average Price ◽  
Scarce Resources ◽  
Corporate Security ◽  
Security Breaches

The purpose of this study is to examine, on intradaily market microstructure basis, fifteen recent occurrences of corporate security breaches to extend our understanding of market efficiency. We document minor average price responses to announcements of a security breach in the firms??target of an attack, contrary to many other corporate announcement studies, which document immediate price reaction to an announcement. Surprisingly, we find that the matching firms in our study have a stronger market microstructure response to the announcement of the attack instead. This study suggests to high-frequency investors, such as hedge funds, that they should focus their attention and scarce resources on developing trading strategies on other corporate events and announcements rather than on the announcement of security breaches.

Institutional Herding: Causality and Persistence

2021 ◽  
pp. 227797522110402
Author(s):  
S S S Kumar
Keyword(s):  
Stock Market ◽  
Market Efficiency ◽  
Mutual Funds ◽  
Institutional Investors ◽  
International Markets ◽  
Jel Classification ◽  
Trading Strategies ◽  
Trading Activity ◽  
Indian Stock Market ◽  
Institutional Herding

We investigate the causality in herding between foreign portfolio investors (FPIs) and domestic mutual funds (MFs) in the Indian stock market. The estimated herding levels are considerably higher than those observed in other international markets, and herding is prevalent in small stocks. We find that institutional investors follow contrarian-trading strategies, unlike what was documented in most other markets. Analysis of the aggregate herding measure shows a bi-directional causality between FPIs and MFs. Further analysis using directional herding measures indicate no evidence of causality between institutional herds on the sell-side. But we find causality on the buy-side and it is running in both directions between FPIs and MFs, implying a feedback of information. Given the tendency of institutions for herding in small stocks, adopting contrarian-trading strategies, the observed sell-side causality is perhaps having a salubrious effect. As institutional investors are contrarians, their trading activity will lead to price corrections in small stocks aligning with the fundamentals, thereby contributing to market efficiency. JEL Classification: C23, C58, G23, G15, G40

Cointegration-based trading: evidence on index tracking & market-neutral strategies

2016 ◽  
Vol 42 (5) ◽  
pp. 449-471 ◽  
Author(s):  
Ioannis Papantonis
Keyword(s):  
Market Efficiency ◽  
Trading Strategies ◽  
Portfolio Performance ◽  
Pairs Trading ◽  
Content Type ◽  
Statistical Arbitrage ◽  
Long Run ◽  
Wide Range ◽  
Short Run ◽  
Depth Sensitivity

Purpose – The purpose of this paper is to present an alternative approach to equity trading that is based on cointegration. If there are long-run equilibria among financial assets, a cointegration-based trading strategy can exploit profitable opportunities by capturing mean-reverting short-run deviations. Design/methodology/approach – First, the author introduces an equity indexing technique to form cointegration tracking portfolios that are able to replicate an index effectively. The author later enhances this tracking methodology in order to construct more complex portfolio-trading strategies that can be approximately market neutral. The author monitors the performance of a wide range of trading strategies under different specifications, and conducts an in-depth sensitivity analysis of the factors that affect the optimal portfolio construction. Several statistical-arbitrage tests are also carried out in order to examine whether the profitability of the cointegration-based trading strategies could indicate a market inefficiency. Findings – The author shows that under certain parameter specifications, an efficient tracking portfolio is able to produce similar patterns in terms of returns and volatility with the market. The author also finds that a successful long-short strategy of two cointegration portfolios can yield an annualized return of more than 8 percent, outperforming the benchmark and also demonstrating insignificant correlation with the market. Even though some cointegration-based pairs-trading strategies can consistently generate significant cumulative profits, yet they do not seem to converge to risk-less arbitrages, and thus the hypothesis of market efficiency cannot be rejected. Originality/value – The primary contribution of the research lies within the detailed analysis of the factors that affect the tracking-portfolio performance, thus revealing the optimal conditions that can lead to enhanced returns. Results indicate that cointegration can provide the means to successfully reproducing the risk-return profile of a benchmark and to implementing market-neutral strategies with consistent profitability. By testing for statistical arbitrage, the author also provides new evidence regarding the connection between the profit accumulation of cointegration-based pairs-trading strategies and market efficiency.

scholarly journals On the Market Efficiency and Liquidity of High-Frequency Cryptocurrencies in a Bull and Bear Market

2020 ◽  
Vol 13 (1) ◽  
pp. 8 ◽  
Author(s):  
Yuanyuan Zhang ◽  
Stephen Chan ◽  
Jeffrey Chu ◽  
Hana Sulieman
Keyword(s):  
Market Efficiency ◽  
High Frequency ◽  
Trading Strategies ◽  
High Frequency Data ◽  
Fluctuation Analysis ◽  
Bear Market ◽  
The Past ◽  
Arbitrage Opportunities ◽  
Rolling Window ◽  
Detrended Fluctuation

The market for cryptocurrencies has experienced extremely turbulent conditions in recent times, and we can clearly identify strong bull and bear market phenomena over the past year. In this paper, we utilise algorithms for detecting turnings points to identify both bull and bear phases in high-frequency markets for the three largest cryptocurrencies of Bitcoin, Ethereum, and Litecoin. We also examine the market efficiency and liquidity of the selected cryptocurrencies during these periods using high-frequency data. Our findings show that the hourly returns of the three cryptocurrencies during a bull market indicate market efficiency when using the detrended-fluctuation-analysis (DFA) method to analyse the Hurst exponent with a rolling window. However, when conditions turn and there is a bear-market period, we see signs of a more inefficient market. Furthermore, our results indicated differences between the cryptocurrencies in terms of their liquidity during the two market states. Moving from a bull to a bear market, Ethereum and Litecoin appear to become more illiquid, as opposed to Bitcoin, which appears to become more liquid. The motivation to study the high-frequency cryptocurrency market came from the increasing availability of higher-frequency cryptocurrency-pricing data. However, it also comes from a movement towards higher-frequency trading of cryptocurrency. In addition, the efficiency of cryptocurrency markets relates not only to whether prices are predictable and arbitrage opportunities exist, but, more widely, to topics such as testing the profitability of trading strategies and determining the maturity of cryptocurrency markets.

scholarly journals ASSESSING THE PREDICTABILITY OF CRYPTOCURRENCY PRICES

2021 ◽  
Vol 25 ◽  
pp. 143-168
Author(s):  
Ruzita Abdul Rahim ◽  
Pick Soon Ling ◽  
Muhammad Airil Syafiq Mohd Khalid
Keyword(s):  
Market Efficiency ◽  
Asset Prices ◽  
Abnormal Returns ◽  
Relevant Information ◽  
Trading Strategies ◽  
The Other ◽  
Market Information ◽  
Great Chance ◽  
The Third ◽  
Daily Returns

The predictability of asset prices works against the notion of an efficient market where asset prices reflect all available and relevant information. This paper examined the predictability of Bitcoin and 51 other cryptocurrencies that have been classified into the following five categories: Application, Payment, Privacy, Platform, and Utility. Two market efficiency tests (Ljung-Box autocorrelation and Runs tests) were run on the daily returns of the 52 unique cryptocurrencies and the MSCI World index from 28 April 2013 to 30 June 2019. The results showed that Bitcoin was consistently efficient, whereas most of the other cryptocurrencies and even the MSCI World index were not, implying that their prices were predictable. Categorically, Payment altcoins were the most consistent in showing inefficiency. Since altcoins in this category also recorded the third highest risk-adjusted returns, investors with advanced technical trading strategies had a great chance of exploiting the market information to make extremely high abnormal returns.

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