Beware of Trading on Your Smartphone

Summary

• A new NBER working paper measures how behaviours such as risk-taking, preference for lottery stocks and trend chasing vary depending on whether investors use a smartphone to trade.
• Smartphone investors are more likely to buy riskier stocks, exhibit gambling-like activity and chase past returns.
• Investors are more prone to changing behaviour on non-smartphone platforms after using smartphone trading apps, buying stocks with higher volatility and more positive skewness.
• The mechanisms driving the results are the ability to trade anytime and anywhere on smartphones, especially after hours, and investors’ preference to use smartphones to trade specific asset classes.

Introduction

A wave of recent literature documents the influence of technology on investor behaviour. For example, Loos et al. (2020) show how robo-advice has the potential to reduce investment biases and improve portfolio performance. And Cen (2019) shows that, after introduction to a mobile trading app, investors’ flows into mutual funds become more volatile and more sensitive to short-term fund returns and market sentiment.

A new NBER working paper measures how behaviours such as risk-taking, preference for lottery stocks and trend chasing vary depending on whether investors use a smartphone to trade. Using transaction-level data from two German banks, they follow investor behaviour within a month, across different platforms (smartphone vs non-smartphone) over a seven-year period. They find:

• The probability of purchasing risky assets increases in smartphone trades vs non-smartphone ones, and smartphone trades involve assets with higher volatility and more positive skewness.
• Smartphones increase the probability of buying assets in the top decile of the past performance distribution, indicating a greater tendency to chase past returns.
• Smartphones also increase the probability of purchasing lottery-type assets (stocks with below-median price and above-median volatility and skewness).

These findings are accentuated during ‘out of hours’ trading, which suggests investors are more prone to making risky trades and chasing trending stocks on smartphones in the evening.

Psychology

New technologies influence behaviour, and in recent years many retail trading platforms have expanded onto smartphones. While this reduces barriers to access and participation costs in the stock market, it could increase financial risk-taking, especially if investors are sensitive to short-term losses.

The authors attempt to determine whether smartphone trading increases fast, instinctive and emotional trading (System 1), or whether it induces more logical and better-informed decisions (System 2). The former would be characteristic of a preference for higher risk and lottery-type trades, while the latter would not.

Literature on the effect of smartphones is increasing. Outside of finance, consumers are more inclined to make impulsive purchases such as ordering more unhealthy food when using mobile devices. Within finance, Robinhood users have been found not to underperform standard benchmarks, perhaps suggesting that smartphones can improve trading performance by increasing access to information and the speed of executions.

Methodology and Data

The paper uses a series of regressions to estimate whether smartphones influence trading behaviours such as risk-taking, preference for lottery stocks and trend following. To measure risk-taking, they estimate the probability of purchasing risky assets (i.e., direct and indirect stock holdings). They use investment skewness, calculated on a 12-month rolling window, and the probability of purchasing lottery-type assets (i.e., stocks with below-median price and above-median volatility and skewness) to proxy gambling preferences. Lastly, for trend chasing, they use the probability of purchasing assets in the top decile of the past return distribution.

The paper’s main benefit is that the authors can observe investor behaviour within a month, across different platforms, and over time. This is because they use transaction-based data, which they collect from two large German retail banks on an estimated 180,000 investors between 2010 and 2017. The data covers the securities traded, type of trade (buy vs sell), date of execution, price of transaction and, importantly, the platform used. From the sample, approx. 10% of investors have used smartphones to trade. Of those, over 15% of trades are placed on smartphones. This percentage increased over time (Chart 1).

Characteristics of Smartphone Traders

While there are no differences in terms of income and wealth, adopters tend to be younger males with shorter tenure at one of the two large retail banks. Specifically, smartphone users have shorter tenure at the bank by one year, are about eight years younger, and are 13% more likely to be males compared with non-users. The percentage of smartphone traders in the sample has also increased over time (Chart 1).

Source: Paper, page 31

Compared with non-users, adopters trade more frequently (10 vs five trades per month) and place larger trades (4,477 vs 3,813 euros). Smartphone users are also more likely to buy riskier assets (95% vs 92%) and purchase more volatile assets (22% volatility vs 17%). Finally, adopters display a higher probability of buying lottery-type assets and investments in the top decile of the past return distribution.

Risk-Taking and Gambling-Like Activity

Comparing trades by the same investor in the same year-month, the authors find that the probability of purchasing risky assets is three to five percentage points (pp) higher for trades done using smartphones relative to other trades (Chart 2). Furthermore, the volatility of assets purchased using smartphones is 4-12pp higher compared with the volatility of other assets.

Source: Paper, page 34

Smartphone users’ preference for lottery-type stocks best captures their higher risk-taking nature. Specifically, using a smartphone increases the skewness of investments by 14-19pp, and smartphone trades increase the probability of purchasing lottery-type assets by 8-10pp.

With regard to chasing hot stocks, using a smartphone increases the tendency of buying assets in the top 10th percentile of past performance. Quantitatively, the probability of buying past winners goes up by 12-16pp when using a smartphone.

Not only do these results show a tendency for smartphone investors to buy riskier assets, investors who have made the switch to a smartphone will transfer these habits away from smartphone platforms. After trading for the first time on a smartphone app, investors’ behaviours on non-smartphone platforms also transition towards greater risk-taking.

Mechanisms Behind the Behavioural Change

Smartphones allow immediate access to trading over an extended period of time. The authors evaluate whether this extended access to trading drives the results. Running the same set of regressions at an hourly frequency, the authors conclude that time of trade is important and that the behavioural effects of smartphone on traders are stronger after hours. That is, traders are more likely to run riskier trades and chase trending stocks after hours.

Interestingly, digital nudges, or push notifications, cannot explain differences in behaviour between smartphone and non-smartphone traders. An example of a digital nudge is the Robinhood trading app featuring the winning and losing stocks of the previous day. Screen size is also found to have no driving influence on risk-taking behaviour.

Bottom Line

Evidence from the paper suggests investors tend to make more intuitive, rather than more logical, decisions while using smartphones, especially later in the day. These decisions are biased towards risky and hot stocks but are uninfluenced by digital nudges. It is, therefore, important to be aware that the convenience attached to trading on a smartphone may come at the price of lower portfolio efficiency and performance.

Citation

Kalda A., Loos B., Previtero A., and Hackethal A. (2021), Smart(Phone) Investing? A within Investor-time Analysis of New Technologies and Trading Behaviour, NBER Working Paper 28363