The Impact of Weather on German Retail Investors*

Abstract

We explore the impact of weather on individual investor trading. Over a time span of 94 months, we analyze daily trading records of individual investors, excluding all automated trades which investors have not triggered such as savings-plan transactions. Controlling for various investor and market specific factors, we find that the effect of weather on the average investor is twofold. Consistent with psychological evidence of a positive impact of pleasant weather on people's mood, we first observe that investor sentiment, as measured by purchases relative to sales, is significantly higher on days with comparatively good weather. In addition to weather's effect on sentiment, we secondly find strong evidence for a relationship between weather and overall trading volume. On days with comparatively bad weather retail investors trade more. This result is consistent with the hypothesis that retail investors incur an opportunity cost for spending time on trading on days with pleasant weather. Using sample splits, we illustrate that the sentiment effect is prevalent for regular traders, whereas occasional traders predominantly incur an opportunity costs of trading.

Fresh Air Eases Work – The Effect of Air Quality on Individual Investor Activity

“…Our study is also related to the literature that analyses investor attention and sentiment. Barber and Odean (2008) show that investors trade because a security has caught their attention by news, because past day's returns were large (Barber and Odean, 2008), Grinblatt and Keloharju, 2001), because the weather is bad (Schmittmann et al, 2014) or because of superstition (Bhattacharya et al, 2014). Da, Engelberg, and Gao (2011) and Da, Engelberg, and Gao (2015) analyze Google search volume and Siganos, Vagenas-Nanos, and Verwijmeren (2014) look at social media mood for Facebook as well as Bollen, Mao, and Zeng (2011) for Twitter and find significant effects on trading volumes at the market level.…”

“…Schmittmann et al (2014) we include several control variables to our panel regression of air quality on different trading variables in order to avoid picking up effects that have already been found in previous studies.…”

Measurement Error in Imputed Consumption

“…This data set has been used and discussed in detail in previous studies (e.g., Schmittmann et al 2014). It consists of a monthly asset position file, a daily transactions file, a file containing bookings to cash accounts, and a file containing investor demographics.…”

Trading on Noise: Moving Average Trading Heuristics and Private Investors

“…Each data record comprises detailed information about individuals' transactions and includes a security identification number (ISIN), a buy/sell indicator, a transaction amount (in euros), a transaction price (in euros), the number of securities traded, the brokerage commissions paid (in euros), the security type, and an investor identification number. We require each analyzed transaction to be actively initiated and therefore neglect all automated transactions (e.g., saving plans, stock allocations from initial public offerings, stock transfers) and thus avoid the risk of finding spurious effects (Linnainmaa, 2010;Schmittmann et al, 2014). Our sample selection is described in Table 1, Panel A.…”