Decision-making under Market Indeterminacy

The Efficient Markets Hypothesis (EMH) is the focusing topic in the past 50 years of financial market researches. Many empirical studies are then provided that want to test EMH but have no consensus. The perception of EMH determines the attitude and strategy of participants and regulators in financial market. One perception of EMH argues that investors’ behavior of seeking abnormal profits and arbitrage drives prices to their ‘‘correct’’ value. Investigating the “correct” value derives the concept of “market indeterminacy”. It means the inability to determine whether stock prices are efficient or inefficient. Market indeterminacy pervades stock markets because “correct” prices are unknown because of imperfect information and model sensitivity. Market indeterminacy makes arbitrage risky and makes event studies unreliable in some policy and litigation applications. The concept of market efficiency is needed to be re-recognized considering the mechanism of price formation. In order to further research and practice in law and financial market, there needs a view from the “jumping together” of disparate disciplines. Adaptive Markets Hypothesis(AMH) that using the evolutionary principles in financial market is a new viewpoint oncognitive decision and deserves to be paid more attention to.

A study of efficiency in New Zealand election prediction markers

<p>The development of prediction markets has naturally given rise to studies of their efficiency. Most studies of efficiency in prediction markets have focused on the speed with which they incorporate information. A necessary (but not sufficient) condition of efficiency is that arbitrage opportunities must non-existent or transitory in nature so that the systematic generation of abnormal profits is not possible. Using data from New Zealand’s first prediction market, iPredict, I examine the potential for arbitrage in the contracts for the party vote for the 2011 General Election. Relative to the risk-free interest rate, the returns from arbitrage are generally low, consistent with an efficient market. Regression analysis requires that the data not be subject to the possibility of spurious regressions - something that is not addressed in the literature. After confirming the non-stationarity of the price level and the stationarity of the price changes by the unit root test, I use the iPredict data in conjunction with opinion poll data to test whether the polls impact on market pricing behaviour. Using a number of different model types, I find that the opinion poll data has a very limited impact on market prices, suggesting that the information contained in the poll is largely already incorporated into market prices.</p>

A study of efficiency in New Zealand election prediction markers

<p>The development of prediction markets has naturally given rise to studies of their efficiency. Most studies of efficiency in prediction markets have focused on the speed with which they incorporate information. A necessary (but not sufficient) condition of efficiency is that arbitrage opportunities must non-existent or transitory in nature so that the systematic generation of abnormal profits is not possible. Using data from New Zealand’s first prediction market, iPredict, I examine the potential for arbitrage in the contracts for the party vote for the 2011 General Election. Relative to the risk-free interest rate, the returns from arbitrage are generally low, consistent with an efficient market. Regression analysis requires that the data not be subject to the possibility of spurious regressions - something that is not addressed in the literature. After confirming the non-stationarity of the price level and the stationarity of the price changes by the unit root test, I use the iPredict data in conjunction with opinion poll data to test whether the polls impact on market pricing behaviour. Using a number of different model types, I find that the opinion poll data has a very limited impact on market prices, suggesting that the information contained in the poll is largely already incorporated into market prices.</p>

An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model

In recent years, there has been an increase in demand for machine learning and AI-assisted trading. To extract abnormal profits from the bitcoin market, the machine learning and artificial intelligence (AI) assisted trading process has been used. Each day, the data gets saved for the specified amount of time. These approaches produce great results when integrated with cutting-edge algorithms. The results of algorithms and architectural structures drive the development of cryptocurrency market. The unprecedented increase in market capitalization has enabled the cryptocurrency to flourish in 2017. Currently, the market accommodates totally 1500 cryptocurrencies, all of which are actively trading. It is always possible to mine the cryptocurrency and use it to pay for online purchases. The proposed research study is more focused on leveraging the accurate forecast of bitcoin prices via the normalization of a particular dataset. With the use of LSTM machine learning, this dataset has been trained to deploy a more accurate forecast of the bitcoin price. Furthermore, this research work has evaluated different machine learning methods and found that the suggested work delivers better results. Based on the resultant findings, the accuracy, recall, precision, and sensitivity of the test has been calculated.

The Deterrent Effect of Insider Trading Enforcement Actions

We analyze whether exposure to an SEC insider trading enforcement action affects how insiders trade. We find that following an insider trading enforcement action at one firm, exposed insiders earn significantly lower abnormal profits from their trades at other firms compared to non-exposed insiders. The deterrent effect is stronger when a fellow insider is convicted and is similarly significant both pre- and post-SOX. Following the enforcement event, exposed insiders do not trade less frequently, but do trade significantly fewer shares per trade. Insiders who have witnessed an enforcement action have a lower probability for future conviction than their unexposed peers.

Gold and oil prices: abnormal returns, momentum and contrarian effects

This paper explores price (momentum and contrarian) effects and their timing parameters on the days characterised by abnormal returns and the following ones in two commodity markets. Specifically, using daily gold and oil price data over the period 01.01.2009–31.03.2020 the following hypotheses are tested: (H1) there is a time gap between the detection of an abnormal return day and the end of that day, (H2) there are price effects on the day after abnormal returns occur; (H3) price effects after 1-day abnormal returns have identifiable timing parameters; (H4) the detected timing parameters can be used to “beat the market”. For these purposes average analysis, t tests, CAR and trading simulation approaches are used. The main results can be summarised as follows. Prices tend to move in the direction of abnormal returns till the end of the day when these occur. The presence of abnormal returns can usually be detected before the end of the day by estimating specific timing parameters, and a momentum effect can be detected. On the following day two different price patterns are detected: a momentum effect for oil prices and a contrarian effect for gold prices, respectively. These effects are limited in time, and the corresponding timing parameters are estimated. Trading simulations show that these effects can be exploited to generate abnormal profits with an appropriate calibration of the timing parameters.

The Relationship Between Gold and Stock Markets During the COVID-19 Pandemic

This chapter aims to analyze portfolio diversification in the US, Europe, UK, Hong Kong, China, Japan, and the gold market (XAU) from January 2019 to July 2020. The results indicate that the markets have very significant causalities, which may call into question efficient portfolio diversification strategies. The DFA exponent coefficients suggest that the random walk hypothesis is rejected in certain markets, which has implications for investors, since some returns can be expected, creating opportunities for arbitrage and abnormal profits. These findings also open space for market regulators to take action to ensure better information among international financial markets. In conclusion, the authors believe investors should diversify their portfolios and invest in less risky markets in order to mitigate risk and improve portfolio efficiency.

Foreign Exchange Market Shocks in the Context of the Global Pandemic (COVID-19)

This chapter analyzes the efficiency, in its weak form, in the international exchange markets from January 1st, 2019 to July 21st, 2020. The results show that the foreign exchange markets show very high levels of integration, which may jeopardize portfolio diversification as well as possible hedging operations. The detrended fluctuation analysis (DFA) shows that the EUR.GBP, GBP.USD, USD.REAL foreign exchange markets show some signs of (in)efficiency showing persistence in yields, while the EUR.JPY, EUR.USD, JPY.CHF, USD.CHF, USD.JPY markets show signs of anti persistence (i.e., the existence of short memories). The USD.BITCOIN, USD.CAD markets do not reject the random walk hypothesis, that is, they are in equilibrium. By way of conclusion, the authors show that the uncertainty of the 2020 pandemic crisis has affected the memory properties of the foreign exchange markets since some returns can be expected, creating opportunities for arbitrage and abnormal profits.