THE SEARCH FOR TIME-SERIES PREDICTABILITY-BASED ANOMALIES

This paper introduces a new algorithm for exploiting time-series predictability-based patterns to obtain an abnormal return, or alpha, with respect to a given benchmark asset pricing model. The algorithm proposes a deterministic daily market timing strategy that decides between being fully invested in a risky asset or in a risk-free asset, with the trading rule represented by a parametric perceptron. The optimal parameters are sought in-sample via differential evolution to directly maximize the alpha. Successively using two modern asset pricing models and two different portfolio weighting schemes, the algorithm was able to discover an undocumented anomaly in the United States stock market cross-section, both out-of-sample and using small transaction costs. The new algorithm represents a simple and flexible alternative to technical analysis and forecast-based trading rules, neither of which necessarily maximizes the alpha. This new algorithm was inspired by recent insights into representing reinforcement learning as evolutionary computation.

Bundling of Ecosystem Services in Conservation Offsets: Risks and How They Can Be Avoided

Conservation offsets are increasingly used as an instrument to conserve biodiversity and ecosystem services on private lands. Bundling ecosystem services (ES) in the market transactions saves costs but implies that only the bundle of ES is conserved while individual ES may decline. This paper presents a simple model analysis of a conservation offset scheme to identify conditions under which bundling can lead to such undesired declines. As it turns out, these are favoured by rarity of the ES as well as a positive correlation between their abundance and the cost of their conservation. A market rule is proposed that is able to avert undesired ES declines. Rather than on sums or means of ES, this market rule focuses on the least abundant ES. Systematic variation of model parameters shows that this trading rule is most effective in those cases where the likelihood of undesired ES losses is highest.

Developing a Single Indicator or Multiple Indicator Market Timing System

In this chapter, the authors use genetic algorithms (GAs) to optimize the parameters of the trading system, which is made by various technical indicators. These trading systems or rules will give buy or sell signals when applied on past prices of a particular stock. Genetic algorithms (GAs) have an ability to find optimal trading indicators that will predict the market direction or trend with greater accuracy. Use of genetic algorithms (GAs) in conjunction to a trading rule refutes efficient market hypothesis (EMH) in a weak form.

Synergistic Market Analysis, Technical Analysis, and Various Indicators

Most of the stock and financial market analysis uses past or historic data in order to forecast the future market indices. This study of past or historic data in order to infer certain value addition from it is known as technical analysis. In this chapter, the authors study a large number of popular indicators. A trader uses these indicators individually or in combination with other indicators to make a trading rule. They test them on past data and choose particular parameters and indicators that gave more profit and drop those that are loss making in nature. Thus, the decision to make entry and exit is given by these technical indicators. This chapter gives a detailed explanation of the most popular technical indicators in use.

Moving Average Convergence-Divergence (MACD) Trading Rule: An Application in Nepalese Stock Market "NEPSE"

There are two types of analysis done for a stock market. One is fundamental analysis, where an investor looks at an intrinsic value of the stock, and another is technical analysis, where investors determine the future trend of the market looking at the current pattern or trend of the market. This paper is focused on one of the technical analysis tools, i.e., Moving Average Convergence-Divergence. It is a tool based on the three exponential moving average (9-12-26 EMA Rule). The MACD analysis, with the help of a single line, was helpful to find out the exact bullish and the bearish trend of the Nepse. A signal line is a benchmark to determine the stock market moving either to a bullish or bearish trend. It can help an investor, where the market is going in a direction. A market convergence, divergence, and crossover were better identified with the help of the MACD histogram. The paper found that the Nepse return was stable for a very minimal period from 1998-99 to 2019-20. The shift from the bullish to bearish or vice-verse were seen easily identified with the help of a MACD histogram. Finally, a better-combined knowledge of moving average and candlestick chart analysis will help an investor, to put a clear picture of a market trend with the help of MACD analysis.

Insurance Options: Beating the benchmark. Are Catastrophe Bonds more profitable than Corporate Bonds?

In this paper we establish a comparison between one of the most traded financial derivatives in the markets, the so-called catastrophe bonds (abbreviated as cat bonds) and the corporate bonds. In the first section, we start from a brief definition as well as some basic concepts. In section two, we will enumerate the type of investors to whom these products might interesting and how to price them. Afterwards, in section three we move onto the analysis of the trading rule proposed, that is, the comparison with Corporate bonds, our benchmark, in terms of expected returns. In sections four and five, we will point out some key issues on how the credit risk associated to these products can be reduced and, finally, in the last section, we will conclude with some discussions and remark the state-of-the-art research on this field.

A Test of Using Markov-Switching GARCH Models in Oil and Natural Gas Trading

In this paper, we test the use of Markov-switching (MS) GARCH (MSGARCH) models for trading either oil or natural gas futures. Using weekly data from 7 January 1994 to 31 May 2019, we tested the next trading rule: to invest in the simulated commodity if the investor expects to be in the low-volatility regime at t + 1 or to otherwise hold the risk-free asset. Assumptions for our simulations included the following: (1) we assumed that the investors trade in a homogeneous (Gaussian or t-Student) two regime context and (2) the investor used a time-fixed, ARCH, or GARCH variance in each regime. Our results suggest that the use of the MS Gaussian model, with time-fixed variance, leads to the best performance in the oil market. For the case of natural gas, we found no benefit of using our trading rule against a buy-and-hold strategy in the three-month U.S. Treasury bills.