Turn-of-the-month effect, FX influence, and efficient market hypothesis: new perspectives from the Johannesburg stock exchange

ABSTRACT

This paper examines the Turn of the Month (TOM) effect in the highly capitalized emerging South African stock market. We use data from the FTSE/JSE Afr8ica All Shares Index (JALSH) and the USDZAR FX market for the period 31.12.1998-31.12.201. We provide empirical evidence that TOM is present in the S. African stock market, but there is a non-TOM anomaly in the FX market. Thus, the S. African stock market enables us to gain new perspectives on the study of the TOM effect. Specifically, using an optimization algorithm, we are able to identify the optimal intra-month period in the JALSH by examining it in both the local currency (ZAR) and in USD. Moreover, we show that the performance in the USDZAR FX market has an impact: (a) on the domestic stock market’s performance (JALSH in ZAR), and (b) on the TOM effect. Finally, we present some practical investment strategies based on the TOM effect which can outperform the stock market and prove beneficial for investors trading in USD.

KEYWORDS:

• Turn of the month
• calendar effects
• efficient market hypothesis
• investment strategies
• FX
• Granger Causality

JEL CLASSIFICATION:

• F31
• G11
• G14

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. The are several calendar anomalies, such as the Day of the Week Effect (Brusa and Liu (2004), Vasileiou (2015), Du Toit, Hall, and Pradhan (2018)), month and trading month effect (Ariel (1987), Vasileiou and Samitas (2015)), but in this paper we focus on the Turn of the Month Effect.

2. (−1, +3) is the period that extends from the last trading day of the previous month up to the first three trading days of the examined month.

3. Capitalization USD 1,278 trillion (January, 2018, source: www.world-exchanges.org).

4. Data source: www.world-exchanges.org (January, 2018).

5. The FTSE/JSE Africa All Shares Index is a market capitalization-weighted index. Companies included in this index make up the top 99% of the total pre-free-float market capitalization of all listed companies in the Johannesburg Stock Exchange (JSE). All the data were gathered from Bloomberg.

6. We should mention that South Africa had different currency regimes in the last decades. In March 1995, the country’s exchange rate system changed from the dual (commercial and financial Rand) era to the floating one. That is one of the reasons we examine the specific data range. In detail, the South Africa ZAR had a controlled floating exchange rate from 1995 to 1999, and since 1999, the ZAR has been allowed to float freely. However, this study’s scope is not to examine the determinants of the USDZAR exchange rate (for more information see Gossel and Biekpe (2012)).

7. If a TOM-strategy does not outperform the BH, the EMH is not violated.

8. If there is a growth period, a strategy which is based on a 4-day investment period may not outperform a BH, because even if we assume that the Non-Turn of the Month (NTOM) days do increase a portfolio’s returns, the transaction costs of such a strategy significantly reduce the profits. So, in this case the increased transaction costs may reduce the possible benefits, which is in favour of the EMH.

9. To assess the robustness of the test, we examined several lag-length criteria and optimizations, most of which produced] similar results.

10. We present only the top 10 intra-month periods to avoid confusing the reader by including too many results. The results of all the combinations are available upon request.

11. We reach similar conclusions as Sharma and Narayan (2014) who observed that the OLS-based models present heteroskedasticity issues. These data are available upon request.

12. The appropriateness of the E-GARCH model validates the assumptions regarding the unsuitability of the OLS model because the volatility is not constant over time (volatility clustering). The leverage effect we observed before is the reason why the Asymmetric GARCH models are more appropriate than the simple GARCH.

13. The transaction cost effect is well-known in the financial economics field of study. For example, this is a contentious issue between chartists and EMH supporters. Fama and Blume (1966) suggest that Technical Analysis failed to outperform the Buy-and Hold (BH) strategy in the late 1950s after adjusting for transaction costs. Sweeney (1988) re-examined the same sample of stocks for a later period, and for a low, but feasible transaction cost, and questioned the results by Fama and Blume (1966).

14. Anyone who is interested in such investment policies may test which strategy is more appropriate for him/her by examining a cost-benefits analysis.

15. For example, the TOM (−1,+3) strategy for the JALSH in the USD case if we assume that the transaction costs eliminate the outperformance. In such a case the BH returns are equal to the final returns of Strategy 1. The same returns could be achieved with less risk. Why is this scenario not a form of EMH violation?

16. When a stock market presents an almost constant growth period a TOM-based strategy cannot outperform the BH, because even if these 4 days are on average more profitable than the rest, they are only 4 days (whereas the BH strategy includes 20 up to 23). An additional reason is the transaction cost: a TOM-based strategy includes 2 transactions per month, while a BH strategy only 2 transactions per examined period.

Additional information

Notes on contributors

Evangelos Vasileiou

Dr. Evangelos Vasileiou is an Assistant Professor of finance in the Department of Financial and Management Engineering at the University of the Aegean, Greece. He holds a Bachelor degree in Economics (University of Piraeus), an MSc degree in Financial Analysis (Department of Banking and Financial Management, University of Piraeus), and a PhD in Finance (Department of Business Administration, University of the Aegean). He is a certified financial analyst, and he has working experience in banking and asset management industry. His main research areas are: financial economics, economic modelling, behavioral finance, financial risk, and market efficiency. His work has been published in high quality international refereed journals, such as Journal of Behavioral Finance, Research in International Business and Finance, International Review of Applied Economics, Intelligent Systems in Accounting, Finance and Management, International Journal of Banking, Accounting and Finance, Journal of Financial Regulation and Compliance, Studies in Economics and Finance, Operational Research: an International Journal, Review of Economic Analysis, Journal of Prediction Markets etc.