Is it possible to predict long-term success with k-NN? Case Study of four market indices (FTSE100, DAX, HANGSENG, NASDAQ)

TL;DR

This study investigates whether k-NN can predict long-term success of stock index components using initial price data, finding promising results for HANGSENG and DAX indices but not for others.

Contribution

It introduces a method to predict stock component success using early price data and k-NN, highlighting differences across indices and demonstrating potential for certain markets.

Findings

HANGSENG and DAX show clear separation of winners and losers.

k-NN can evaluate long-term success probabilities for some indices.

No significant separation found for NASDAQ and FTSE100.

Abstract

This case study tests the possibility of prediction for "success" (or "winner") components of four stock & shares market indices in a time period of three years from 02-Jul-2009 to 29-Jun-2012.We compare their performance ain two time frames: initial frame three months at the beginning (02/06/2009-30/09/2009) and the final three month frame (02/04/2012-29/06/2012). To label the components, average price ratio between two time frames in descending order is computed. The average price ratio is defined as the ratio between the mean prices of the beginning and final time period. The "winner" components are referred to the top one third of total components in the same order as average price ratio it means the mean price of final time period is relatively higher than the beginning time period. The "loser" components are referred to the last one third of total components in the same order as they have higher mean prices of beginning time period. We analyse, is there any information about the winner-looser separation in the initial fragments of the daily closing prices log-returns time series. The Leave-One-Out Cross-Validation with k-NN algorithm is applied on the daily log-return of components using a distance and proximity in the experiment. By looking at the error analysis, it shows that for HANGSENG and DAX index, there are clear signs of possibility to evaluate the probability of long-term success. The correlation distance matrix histograms and 2-D/3-D elastic maps generated from ViDaExpert show that the winner components are closer to each other and winner/loser components are separable on elastic maps for HANGSENG and DAX index while for the negative possibility indices, there is no sign of separation.