Appaloosa Management L.P. 13F holdings and portfolio analysis

已就緒Appaloosa Management L.P. · David Tepper

登錄後可基於當前選中的策略克隆一個新分支，繼續在新分支裏調整參數與權重。

官方策略
用户分支
返回基金列表

Baseline

分析消息
分支：
Baselinetested
策略問答參考3 條參考問答

基於這隻基金最新一期數據預生成的問答，可直接作為對話上下文使用。

baseline13FChat AI
What does directly following the disclosed baseline portfolio expose an investor to?

Directly following the baseline means accepting a concentrated 13F clone with benchmark-like market exposure and filing-lag risk. The baseline top 5 concentration is 44.48% and top 10 is 66.68%, led by BABA at 12.69%, GOOGL at 9.44%, AMZN at 8.47%, MU at 7.21%, and META at 6.67%. Sector exposure is concentrated in Consumer Discretionary at 32.92%, Information Technology at 24.64%, and Communication Services at 17.38%. Backtest metrics show beta 1.00, max drawdown -29.75%, and identical optimized/original results, so there is no risk-reduction buffer from reweighting.

↳Ask the app to stress-test the baseline by removing the largest 1–3 holdings and measuring how concentration, drawdown, and return change.
baseline13FChat AI
Which recent baseline periods best explain the risk-return trade-off?

The best recent periods to study are the ones where excess return was strong but turnover and dispersion were high. In 2023-03-31, baseline returned 15.42% versus 7.90% for SPY, a 7.52-point excess return with 36.90% turnover. In 2025-06-30, it returned 11.74% versus 4.20%, a 7.54-point excess return with 46.78% turnover. On the weak side, 2024-03-31 returned -2.06% versus SPY’s 2.31%, a -4.38-point lag, and 2023-12-31 underperformed by -1.13 points despite 22.90% turnover. These periods show the trade-off clearly: alpha came in bursts, but the path was uneven and turnover was often meaningful.

↳Ask the app to decompose 2023-03-31, 2024-03-31, and 2025-06-30 by stock and sector contribution to see what drove the swing in excess return.
baseline13FChat AI
What should a user inspect next before deciding whether the baseline is acceptable?

A user should inspect three things next: concentration, lag sensitivity, and turnover. First, the baseline holds 44.48% in the top five and 88.87% in the top twenty, so position-level risk matters a lot. Second, risk notes explicitly say the backtest uses filing-based implementation, creating a disclosure lag between manager action and copycat trade dates. Third, turnover can spike materially: 94.21% in 2023-06-30, 46.78% in 2025-06-30, and 46.27% in 2025-09-30, with trade counts reaching 36 in the latest listed period. Those three checks matter more than headline return alone.

↳Ask the app to show how much of baseline return survives after filing lag, turnover costs, and a cap on top-holding weights.
登錄後即可基於上方策略與右側數據，直接與 AI 對話分析。
登錄後開始對話…
可參考左欄策略與右欄數據。