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The purpose of this paper is to illustrate the effectiveness of integrating a portfolio simulation‐based trading program with the top‐down approach to fundamental analysis in a security analysis course. The simulation allows for the application of class material using a combination of group and individual projects.

Students enrolled in the class completed a survey about the integrated approach and the required simulated trading.

Over 87 per cent of students agreed that the economic analysis provided more educational value as a group project than as an individual project, while over two‐thirds of the students disagreed that the trading simulation had more education value as a group project.

Although the authors focus on the top‐down approach, the concepts of technical analysis, hedging, and income generation could be more formally incorporated into the trading simulation.

The outline of how to integrate a trading simulation into the top‐down approach, using a combination of group projects and a cumulating project completed by each student, can be used as a guide for how to make the top‐down approach a more meaningful task.

The integration of the portfolio simulated trading program with the top‐down approach makes the course more applied and more enjoyable for both the students and the faculty.

The paper outlines how to integrate a trading simulation and the top‐down approach and reports the finding that students preferred the group approach to economic analysis and individual projects for the simulation and the company analysis.

An agent-based market simulation is utilized to examine the impact of high frequency trading (HFT) on various aspects of the stock market. This study aims to provide a baseline understanding of the effect of HFT on markets by using a paradigm of zero-intelligence traders and examining the resulting structural changes.

A continuous double auction setting with zero-intelligence traders is used by adapting the model of Gode and Sunder (1993) to include algorithmic high frequency (HF) traders who retrade by marking up their shares by a fixed percentage. The simulation examines the effects of two independent factors, the number of HF traders and their markup percentage, on several dependent variables, principally volume, market efficiency, trader surplus and volatility. Results of the simulations are tested with two-way ANOVA and Tukey’s post hoc tests.

In the simulation results, trading volume, efficiency and total surplus vary directly with the number of traders employing HFT. Results also reveal that market volatility increased with the number of HF traders.

Increases in volume, efficiency and total surplus represent market improvements due to the trading activities of HF traders. However, the increase in volatility is worrisome, and some of the surplus increase appears to come at the expense of long-term-oriented investors. However, the relatively recent development of HFT and dearth of appropriate data make direct calibration of any model difficult.

The simulation study focuses on the structural impact of HF traders on several aspects of the simulated market, with the effects isolated from other noise and problems with empirical data. A baseline for comparison and suggestions for future research are established.

Recent research indicates that the random walk hypothesis (RWH) approximately describes the behavior of major dollar exchange rates during the post‐1973 float. The present analysis seeks to examine the profitability of currency futures trading rules that assume that spot exchange rates can be adequately modeled as a driftless random walk.

Two random walk currency futures trading rules are simulated over all available data from the period 1984‐2003. In both cases, the investor buys currencies selling at a discount and sells those selling at a premium, as the RWH implies. The two rules differ only in the way they allocate the hypothetical investor's resources among long and short foreign currency positions.

Results show that an investor who used these trading strategies over the past decade would have enjoyed large cumulative gains, although periods of profit were interrupted by periods of substantial loss.

The findings encourage the hope that profitable random‐walk‐based strategies for currency futures trading can be devised. The simulation results have important implications for those willing to hedge, borrowers, and speculators.

This paper provides evidence that purchasing futures contracts on currencies priced at a discount and selling futures contracts priced at a premium has generally been a profitable trading strategy during the last two decades of floating exchange rates.

This paper aims to demonstrate a dynamic cointegration-based pairs trading strategy, including an optimal look-back window framework in the cryptocurrency market and evaluate its return and risk by applying three different scenarios.

This study uses the Engle-Granger methodology, the Kapetanios-Snell-Shin test and the Johansen test as cointegration tests in different scenarios. This study calibrates the mean-reversion speed of the Ornstein-Uhlenbeck process to obtain the half-life used for the asset selection phase and look-back window estimation.

By considering the main limitations in the market microstructure, the strategy of this paper exceeds the naive buy-and-hold approach in the Bitmex exchange. Another significant finding is that this study implements a numerous collection of cryptocurrency coins to formulate the model’s spread, which improves the risk-adjusted profitability of the pairs trading strategy. Besides, the strategy’s maximum drawdown level is reasonably low, which makes it useful to be deployed. The results also indicate that a class of coins has better potential arbitrage opportunities than others.

This research has some noticeable advantages, making it stand out from similar studies in the cryptocurrency market. First is the accuracy of data in which minute-binned data create the signals in the formation period. Besides, to backtest the strategy during the trading period, this study simulates the trading signals using best bid/ask quotes and market trades. This study exclusively takes the order execution into account when the asset size is already available at its quoted price (with one or more period gaps after signal generation). This action makes the backtesting much more realistic.

On June 27, 2002, the Second Circuit Court of Appeals ruled in Caiola v. Citibank, N.A. (Caiola II) that cash‐settled options are “securities” under Section 3(a)(10) of the Securities Exchange Act of 1934 (Exchange Act). In doing so, the Second Circuit disagreed with the Southern District of Ohio and the lower Caiola court (Caiola I), both of which took the position that certain cash‐settled options based solely on the value of a security were not securities because they “did not give either counterparty the right to exercise an option or to take possession of any security”. The Court adopted the Securities and Exchange Commission’s (SEC) view, widely supported in the securities bar, that “neither the right to take possession of any security nor the right to choose whether to exercise a necessary feature of an option on a security”.

This paper argues that one must be careful in drawing lessons from the development experience of East and Southeast Asia. The economic strategy followed by these countries was not one of free trade, or even simulated free trade. Instead, in these countries key conditions were created which provided hospitable environments for investment coordination. It was the ensuing boom in investment which generated rapid economic development. The current economic problems of this region are not the result of corrupt states or crony capitalism, none of which are new to the region, but the result of an inability to engage in economic restructuring

The study tests the hypothesis that following the arrival of news in the forex market, the trader/dealers demonstrate two kinds of biases which makes markets volatile: “Recurrence bias,” the belief that news which formerly led to volatility, will again generate volatility (i.e. volatility is recurring), and “Volatility Perception Bias,” the belief that increased volatility following the arrival of a news would persist.

The author uses a preliminary survey and three simulated trading game experiments involving professional foreign exchange dealers to understand these heuristic-led biases and the biases' impact on market volatility.

The paper finds evidence supporting the presence of both “Recurrence Bias” and “Volatility Perception Bias” and a statistically significant, positive impact of participant biases' on market heterogeneity.

The paper makes two important contributions: first, the use of simulated trading game experiment involving professional dealers and second, the incorporation of dealers' biases and heuristics in understanding forex volatility.

To investigate the performance of moving average trading rules in an emerging market context, namely that of the Jordanian stock market.

The conditional returns on buy or sell signals from actual data are examined for a range of trading rules. These are compared with conditional returns from simulated series generated by a range of models (random walk with a drift, AR (1), and GARCH‐(M)) and the consistency of the general index series with these processes is examined. Sensitivity analysis of the impact of transaction costs is conducted and standard statistical testing is extended through the use of bootstrap techniques.

The empirical results show that technical trading rules can help to predict market movements, and that there is some evidence that (short) rules may be profitable after allowing for transactions costs, although there are some caveats on this.

New results for the Jordanian market; use of sensitivity analysis to investigate robustness to variations in transactions costs.

The study systematically investigates persistence in performance for simulated trading among non-professional traders in the futures market.

In this study, the authors employ a novel data set from the Chicago Mercantile Exchange (CME) Group's Trading Challenges for years 2014 through 2018 and expand upon the empirical methodology of Malkiel (1995) through improved interval estimations in testing for persistence in performance. The authors implement Fama-MacBeth style regressions to understand the degree of persistence in performance and the extent to which non-professionals extrapolate from prior returns. They adjust returns for risk through the Fama and French (2015) five-factor model in understanding whether the sample of non-professionals is able to produce excess returns after expenses and whether there is evidence of excess gross to cover expenses.

The empirical analysis suggests strong evidence for performance persistence among non-professionals participating in the Preliminary Rounds. In the Championship Rounds, the authors find that the persistence effect becomes stronger in economic and statistical significance after accounting for expenses. The results suggest that competition and transaction costs help to distinguish between winners and losers. When conducting Fama-MacBeth style regressions, the authors present evidence that strongly supports the persistence effect and over-extrapolation. While the results of the multi-factor model analysis suggest that, after adjusting for risk, most teams are experiencing negative excess returns prior to expenses, the authors also uncover evidence of teams earning returns sufficient to cover their expenses.

The authors bridge the gap between the literature on performance persistence and the emerging literature on non-professionals in the financial markets. Data from the CME Group’s Trading Challenge provide a rich source in studying the beliefs of non-professionals, and this study is helpful for understanding how beliefs, operationalized in simulated trades, perform over short time horizons, thereby providing insights into the behavioral dynamics of the financial markets. The results provide new empirical evidence for performance persistence among non-professionals.