摘要：Often, a relatively small group of trades causes the major part of the trading costs on an investment portfolio. For the equity trades studied in this paper, executed by the world’s second largest pension fund, we find that only 10% of the trades determines 75% of total market impact costs. Consequently, reducing the trading costs of comparatively few expensive trades would already result in substantial savings on total trading costs. Since trading costs depend to some extent on controllable variables, investors can try to lower trading costs by carefully controlling these factors. As a first step in this direction, this paper focuses on the identification of expensive trades before actual trading takes place. However, forecasting market impact costs appears notoriously difficult and traditional methods fail. Therefore, we propose two alternative methods to form expectations about future trading costs. The first method uses five ‘buckets’ to classify trades, where the buckets represent increasing levels of market impact costs. Each trade is assigned to a bucket depending on the probability that the trade will incur high market impact costs. The second method identifies expensive trades by considering the probability that market impact costs will exceed a critical level. When this probability is high, a trade is classified as potentially expensive. Applied to the pension fund data, both methods succeed in filtering out a considerable number of trades with high trading costs and substantially outperform no-skill prediction methods. The results underline the productive role that model-based forecasts can play in trading cost management.
