“The strategy invests in NYSE/Amex/Nasdaq-listed securities, identifying foreign sales activities via 10-K reports. Stocks are sorted into deciles based on industry peers’ lagged returns, with monthly rebalancing.”
ASSET CLASS: stocks | REGION: United States | FREQUENCY:
Monthly | MARKET: equities | KEYWORD: Sales
I. STRATEGY IN A NUTSHELL
The strategy invests in NYSE/Amex/Nasdaq-listed securities, excluding financial firms and those with stock prices under $1. To identify firms’ foreign sales activities, the paper follows the Hoberg and Moon (HM) methodology. This involves analyzing 10-K reports to capture mentions of foreign countries and examining the context of words near each mention (such as “export” or “customer”) to identify offshore sales activities. The firm’s output market distribution is derived from the frequency of mentions, and proximity between firms is calculated using cosine similarity. The strategy links offshore sales network returns to the proximity-weighted average monthly return of industry peers. Stocks are sorted into deciles based on lagged returns from their industry peers, going long on the top decile and short on the bottom. The strategy is value-weighted and rebalanced monthly, with offshore and accounting data from year t matched to stock returns from the following year.
II. ECONOMIC RATIONALE
The strategy leverages the fact that industry peers with overlapping overseas sales destinations are exposed to similar regional economic and political shocks, impacting their stock prices. A key reason for this inefficiency is the insufficient regulatory requirement for reporting detailed foreign operations. The complexity of dynamic offshore networks makes it harder for investors to identify offshore sales network (OSN) relations between firms, leading to slow information incorporation. Research shows strong return predictability across OSN-linked firms. Additionally, abnormal returns from the strategy cannot be explained by asset pricing models (CAPM, Carhart, FF3, FF5), and the alpha of the long-short strategy remains significant and stable.
III. SOURCE PAPER
Offshore Sales Networks and Stock Return Predictability [Click to Open PDF]
John (Jianqiu) Bai, Northeastern University – D’Amore-McKim School of Business; Priya Garg, University of San Diego – Department of Finance; Chi Wan, University of Massachusetts Boston – Department of Accounting and Finance
<Abstract>
Based on 10-K textual analysis, we assemble firm-level offshore sales networks (OSN) and find strong return predictability among industry participants that have overlapping offshore sales activities. This intra-industry return predictability based on offshore sales networks is distinct from that along several previously documented economic linkages (e.g., industry momentum, technological links, and standalone vs conglomerate firms. Moreover, we find that the effect is stronger for firms that receive low investor attention, issue hard-to-read 10-Ks, and pose high arbitrage costs. Our results highlight important asset pricing implications of the commonality of corporate offshore activities, and are broadly consistent with sluggish price adjustment caused by investors’ inattention to offshore networks.
IV. BACKTEST PERFORMANCE
| Annualised Return | 14.99% |
| Volatility | 23.03% |
| Beta | N/A |
| Sharpe Ratio | 0.65 |
| Sortino Ratio | N/A |
| Maximum Drawdown | N/A |
| Win Rate | N/A |
