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Asset Price Dynamics, Volatility, and Prediction

We make three contributions to using the variance ratio statistic at large horizons. Allowing for general heteroscedasticity in the data, we obtain the asymptotic distribution of the statistic when the horizon k is increasing with the sample size n but at a slower rate so that k/n → 0. The test is shown to be consistent against a variety of relevant mean reverting alternatives when k/n → 0. This is in contrast to the case when k/n → δ >0, where the statistic has been recently shown to be inconsistent against such alternatives. Secondly, we provide and justify a simple power transformation of the statistic which yields almost perfectly normally distributed statistics in finite samples, solving the well known right skewness problem. Thirdly, we provide a more powerful way of pooling information from different horizons to test for mean reverting alternatives. Monte Carlo simulations illustrate the theoretical improvements provided.

The Variance Ratio Statistic at large Horizons

We conduct a comprehensive study of commonality in liquidity using intraday spread and depth data from 47 stock exchanges. We find that firm-level changes in liquidity are significantly influenced by exchange-level changes across most of the world's stock exchanges. Emerging Asian exchanges have exceptionally strong commonality, while those of Latin America exhibit little if any commonality. After documenting the pervasive role of commonality within individual exchanges, we examine commonality across exchanges. We find evidence of a distinct, global component in bid-ask spreads and depths. Local (exchange-level) sources of commonality represent roughly 39% of the firm's total commonality in liquidity, while global sources contribute an additional 19%. We also investigate potential sources of exchange-level and global commonality. We show that commonality is driven by both domestic and U.S. macroeconomic announcements.

Commonality in Liquidity: A Global Perspective

One of the three aspects that influences the success of the stock investment, uses to understand the various data and information available in the financial market for investor’s decisions. There is two analysis, fundamental and technical. The fundamental analysis applies Net Income, Equity, and Cash Dividend, while technical analysis applies Average Prices and Volume. All of these variables will be examined their effect on the Corporate Market Capitalization. Samples are stocks that have been or currently in the LQ45 index for the period 2008 - 2018. This research utilized path analysis for the analytical method, on the SPSS program. All variables need to be transformed to get through the normality test. It transformed again with the Cochrane Orcutt method because of autocorrelation. The method applied outlier data tests, multicollinearity, the Sobel test without heteroscedasticity. The results show that Net Income and Equity simultaneously or individually, have a positive and significant effect on Cash Dividend, which is a mediating variable to the Corporate Market Capitalization. Furthermore, Net Income, Equity, Cash Dividend, Average Price, and Volume simultaneously or individually, have a positive and significant effect on the Corporate Market Capitalization.

The Effect of Net Income, Equity, Cash Dividend, Average Price and Volume to Corporate Market Capitalization Stocks in LQ45 Index of Indonesia Stock Exchange Period 2008 – 2018

This paper employs the Moving Average (MA) method which is a technical analysis designed to generate a rule to develop appropriate investment positions at any given time. In context of market index, it works with a comparative analysis of most recent index level (short MA) with the contrary long MA of the index. A short MA (one day) in comparison with long MAs is the focus of this study, in conjunction with ‘zero filter’. The results are compared against the efficient market hypothesis which confirms that if technical analysis has a significant value it is a sign of an inefficient market at weak form. The case study of the five Jordanian indices in the market strengthens the hypothesis as it demonstrates that the application of MA technique allows significant returns as the predictability of the market is enhanced. The market analysis from January 1, 2008 to December 31, 2016 has proven that ASE is inefficient at weak form level thus offering opportunities to traders to make abnormal profits.

Market Efficiency Analysis of Amman Stock Exchange through Moving Average Method

A general issue with moving average trading is the assumption that all buy/sell signals result in a trading action. The argument that such trading rules are representative of trading practice is highly questionable. This thesis proposes two new moving average trading rules designed to capture trading practice. The first trading rule is the trade reduction rule and is based on the idea of allowing a trade to run. The second trading rule is the positive autocorrelation rule and is based on the idea of only trading if it is believed to be profitable to do so. The trading rules are tied to moving average trading via the buy/sell signal generating mechanism and alter the way the price crossover rule responds to the buy/sell signals. Simulations of portfolios of UK equities find that the trading rules uncover information that is missed by the price crossover rule and there is evidence that this information is financially exploitable. This motivates the argument that the information needed for trading to be economically viable is observable in the price. The trading rules also establish a link with the market microstructure literature. The trading rules uncover issues of informed trading (asymmetric information), liquidity, adverse selection and price impact. The strongest interpretation that can be applied to the trading rules in this context is that they are examples of informed trading. Compared to the price crossover rule, the trading rules are better able to extract meaning from or are better able to understand the same price information.

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Marcus Christian Toms

Papers

The technical analysis method of moving average trading : rules that reduce the number of losing trades