What makes an investment risky? An analysis of price path characteristics

Abstract

We examine the influence of financial asset historical price path characteristics on investors' risk perception, return beliefs and investment propensity. To that end, we run a series of survey experiments in which we present various price patterns to individuals with vested interest in financial matters. Our findings reveal that price paths with identical daily and monthly returns (and consequently identical return standard deviation) can lead to substantially different risk perception by investors, indicating that historical volatility is insufficient to explain risk perception. Salient features such as highs, lows and crashes are the most influential drivers of perceived risk in price paths. Return forecasts are primarily driven by past overall returns and the most recent price developments. Perceived risk and return beliefs strongly predict investment propensity.

Reference point formation: Does the market whisper in the background?

“…This does not mean that subjects necessarily compute or even conceptualize a formal covariation parameter such as beta or correlation, as the editing stage operates unconsciously on the basis of emotion and heuristic simplification to set the stage for cognitive processing. The notion that subjects perceive volatility or comovement information directly from the visual pattern in the editing stage without formal computation of statistical parameters is consistent with Duxbury and Summers (2018), Borsboom and Zeisberger (2020), and Sobolev and Harvey (2016), as well as Grosshans and Zeisberger (2018) who state that the specific mechanism in prospect theory for the cognitive biases they address is the weighting function. In addition, reference point formation in general may reflect aspirational thinking as mentioned by BWW, or other factors such as anchoring or attention.…”

“…Furthermore, they report evidence to support the view that the extent to which prices appear irregular is a separate aspect of volatility, distinct from the extent to which prices deviate from central tendency. Borsboom and Zeisberger (2020) show that investors focus on heuristics in looking at visual stock price patterns and that investor risk perceptions are driven by salient features of price path characteristics such as high and low prices and short-term crashes rather than standard deviation, consistent with Duxbury and Summers (2018). They also document investor belief in short-term trend continuation consistent with end-anchoring.…”

Robo-investment aversion

“…Participants saw a series of historical price paths [ 43 , 44 ] and were asked to decide whether they would prefer their investments in these stocks to be managed by a group of humans with investment knowledge, or by an algorithm (using the nnetar function from the forecast package in R [ 45 ], which trains a neural network on the historical price paths to forecast future prices). The knowledgeable humans were the 11 most effective performers in a pilot study ( N = 101), carried out earlier on Mechanical Turk: we assessed their investment knowledge using a very brief test (with a maximum score of 3; see Experimental Materials on OSF for details), and then told them to predict the price of the stocks two months after the shown 1-year period (252 trading days).…”

A new hedging hypothesis regarding prediction interval formation in stock price forecasting

“…Therefore, we added the respective measures of salient features of past price series in our regressions to control for their effects. The salient features we considered included extreme points (Mussweiler & Schneller, 2003), crashes (Borsboom & Zeisberger, 2020), last trade direction (Duclos, 2015; Sobolev & Harvey, 2016), run length, local minima and maxima (Raghubir & Das, 2010), mean absolute price change, and irregularity (Duxbury & Summers, 2018). Second, we included the price chart fixed effects in the regressions.…”

“…Previous literature shows that various salient features of the presented time series affect subjects' forecasts/decisions. Such features include price path patterns (Grosshans & Zeisberger, 2018; Huber & Huber, 2019), extreme points (Mussweiler & Schneller, 2003), crashes (Borsboom & Zeisberger, 2020), last trade direction, uncertainty level (Duclos, 2015; Sobolev & Harvey, 2016), run length, local minima and maxima (Raghubir & Das, 2010), and the mean absolute price change and irregularity (Duxbury & Summers, 2018) of past price series. While these salient time‐series features have the potential to affect the length and asymmetry of JPI, our focus is on the role of the belief wedge to explain the asymmetry of JPI, and we control for these features, insofar as this is possible, in our empirical analysis.…”