Disclaimer: I used the available market data during Bearish Market in 2019. However, these techniques can be applied to other market condition as well. The tutorials referred were mostly performed using MS Excel.
COVID-19 pandemic, which has infected since the end of 2019, has knocked out capital markets around the world, including in Indonesia. Also during the pandemic, I heard people start competing to offer various safe investments. Considering to its impact to Indonesia economic, my benchmark is naturally directed towards investments in capital markets. On the other hand, stock market movements could also represent future economic prediction.
Indonesia economy is currently projected to shrink about 3.1 percent in the second quarter. In line with the current economic circumstance, exchange markets experienced weakness and stock prices plummeted (bearish market) for months ever since the outbreak. Thus, the assumption of risk and return of stock portfolio will be slightly different from the assumptions under bullish markets. During the bearish market, diversification of portfolio-forming stocks must succeed in reducing the risk level.
Reflecting on Investment during the pandemic, I can only perform the simulation based on last year’s data since I do not have fully access of daily returns of Indonesian Stocks. The market was down on September 2019, making most of stock prices LQ45 Index dropped. Although the market crash happened and psychological effects were not as bad as during the pandemic, the simulation will show same pattern as in other bearish markets.
Portfolio simulation: Why behavioural approach is necessary
Often times investors prior decision were made through their own information bubbles. Their tendency to only choose familiar stocks to form portfolio is called salience. At first, study on the formation of a stock portfolio based on salience was conducted by Da Silva Rosa and Duran (2008), which is based on the number of times a stock is reported in a major national newspaper. In his study, it was found that even rational investors rely on salience to conduct portfolio formation.
During the post-truth era, facts and evidence have frequently been replaced by personal belief and emotion. The development of digital technology, such as Artificial Intelligence (AI), helps us to filter zillion of news on the internet and tries to recommend us news related to our preferences. This personalization makes us rarely receive news that we do not prefer to see.
Indonesia by any chance is one of the countries with high internet growth. A survey conducted by We are social (2019) showed out of a total of 287 Million Indonesians, the number of internet and active social media users were about 150 million each. Based on Indonesian Digital Report 2020, Indonesia already has around 160 million active social media users (59% of all Indonesia population). Internet used for digital economic activities are dominated by price searching activities (45.14%), helping jobs (41.04%), buying information (37.82%), electronic buying transactions (32.19%), job search (26.19%), banking transactions (17.04%), and online sales (16.83%).
To add the urgency, many investors in this social media generation, are not equipped, or even are not interested, with news literacy to understand the news with critical thinking, to analyse and judge the reliability of news and information and differentiate among facts, opinions, and assertions in the media. They tend to invest the stock based on their own familiarity. Hence, I will also add salience criteria in forming portfolio instead of only using rational decision. Based on OECD’s Programme for the International Assessment of Adult Competencies (PIAAC) score, adults in Jakarta show low levels of proficiency in literacy compared to adults in other countries and economies that participated in the same survey. Hence, Indonesia is in an emergency to handle the massive spread of news circulated through social media
The timeline I used to observe the stocks during portfolio formation is devided into 1,2, and 3 months observation with the assumption that the longer the period, the higher the returns of investment. Since I can only observe salience effect at November 2019 while the Bearish happened at September 2019, my aim is only to get insights about performance simulation of portfolio formed through salience then compare its results to rational portfolio (Mean-Variance Markowitz) in hoping that data processed could give abnormal return far from the actual return.
Both portfolios will get the same treatment. The only difference is the method of selecting portfolio-forming stocks. Rational Portfolio will be formed based on P/E Ratio of all stocks listed at Indonesia Stock Exchange. Salience portfolio is formed using data from Twitter (Total company post, Total company followers, Total number of mentions) and SVI (Search Volume Index) of Google Trend. Performance simulation is based on market data collected during September 2019 when market was bearish. Last, the data are tested three times, 1 month observation (during Aug 2019), 2 month observation (Jul-Aug 2019), and 3 month observation (Jun-Aug 2019).
I am selecting 10 company stocks listed at IDX sorted by lowest P/E ratios, as a proxy for risk-to-return ratio, still with positive average return in order to form rational portfolio. Three criteria of stocks are considered only if 1) stocks must be listed at IDX during Portfolio formation. Each stock must have lowest Price to Earnings (P/E) ratio and positive returns for 1, 2, and 3 months of observation. Of the ten selected stocks, several industries related to this portfolio include the infrastructure, property, manufacturing, services, agriculture, energy and financial services industries.
Required behavioural aspects on portfolio formation will be conducted mainly through Google Trends and Twitter. Since I am going to utilise two tools, there are more criteria needed on selecting the stocks. Since I dont have a tool to screen hundreds of company’s website and social media, I will limit stocks observation to only those which were a part of LQ45 Index during the portfolio formation. Of the 45 stocks, the stock must have the highest Google Trends’ Search Volume Index (SVI) and official Twitter Account. Both observations of google trends and Twitter are conducted during the period of one week, from 23-29 November 2019.
Google trends analysis is conducted by inputting the issuer’s code in the search term column as the keyword. Search Volume Index of each issuer was then sorted from the highest to the lowest Index. Meanwhile, Twitter activity analysis is carried out by utilising a trial version of TweetBinder. I input each company name as keywords to analyse the conversations occurred on Twitter.
Of ten stocks, five companies are from financial service sectors. Also since I used the trial version of Tweet Binder, Maximum Twitter Mention calculated are 500 mentions. The table shows that generally SVI of Google Trends and Twitter Activities of Banking Industry are higher than those who are not.
Both portfolio returns are calculated for a period of 1 month, 2 months and 3 months before the bearish market. Stock Returns were collected from Thompson Reuters. I decide to collect daily returns of the chosen stocks in order to minimise standard error during statistical calculations. Then the covariance and variance of the two portfolios are processed using data analysis tool in MS Excel. In order to create optimum portfolio returns, stock weights will be calculated by Solver.
Optimum Returns of Rational and Behavioural Portfolios in Bearish Market
In order to gain higher returns, the above table shows that sharpe ratio of salience portfolio averagely higher than rational portfolio. The longer investors hold the money on markets also potentially give higher returns. However, bearish market cause abnormal returns depicted on Cronbach’s alpha to be negative, meaning that investors will gain negative returns instead of positive returns. Also the longer the period observation, the more negative the returns. Salience portfolio also gives higher negative abnormal returns compared to rational portfolio.
Table 4 shows risks could be minimised in rational portfolio for portfolio-forming stocks are well-diversified . In contrast to salience portfolio, structured stocks do not offer diversification, causing a greater negative return compared to rational portfolio. A well diversified portfolio shows low covariance. Stocks which have high covariance tend not to be considered in the portfolio, causing the portfolio not to be “robust”, although it has better performance than rational portfolio.
How to avoid high loss
There are some strategies which can be considered in order to reduce risks in Bear Market.
- Defensive strategy. Consider to start investing in large company with strong balance sheet and long operational history i.e large market capitalization and stable growth. Defensive stocks also involve products which could be consumed over times such house hold goods, toiletries, medicines, etc. By investing in this company, no matter how bear the market is, people will never stop buying these products.
- Buying more stocks at much cheaper prices. This decision must be defined based on investors financial constraints. During the bear market, moreover crisis caused by pandemic, cash always become a king. Even tho the price would rise after the crisis, making sure that they have stack of cash is mostly very important.
- Investing at more stable instruments such as Government Bond and Fixed Deposit. In Indonesia, I personally will choose Government Bonds rather than fixed deposits since most of the time the returns of Government Bonds are higher than Fixed Deposits. I cannot recommend investing at company’s bonds even tho most of the time the returns are not that higher compared to Gobernment Bonds, the risks usually are much higher.
- In my experience, I also do not recommend investing in Gold for its price tend to peak higher during the crisis while in the same time, buyback is not easy due to the more closed store and so it becomes much less liquid.
- Bank, S., Yazar, E. E., & Sivri, U. (2019). Can social media marketing lead to abnormal portfolio returns?. European Research on Management and Business Economics , 25 (2), 54-62.
- Barber, B. M., & Odean, T. (2001). The internet and the investor. Journal of Economic Perspectives , 15 (1), 41-54.
- Bodie, ZVI, et al. 2014. Investments, 9th edition . New York: McGraw Hill
- Da Silva Rosa, R., & Durand, R. B. (2008). The role of salience in portfolio formation. Pacific-Basin Finance Journal , 16 (1-2), 78-94.
- Fama dan French, (2020). “Fama and French 3 Factors for Asia-Pacific,” diunduh dari http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html#International pada 22 July 2020.
- IDX. (2019). Daftar Indeks Saham LQ45 Agustus 2019 – Januari 2020. Diunduh dari https://www.idx.co.id/media/7599/lq45-company-profiles-august-2019.pdf pada 22 July 2020.
Photo by Markus Spiske on Unsplash
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