The Positive Similarity of Company Filings and Stock Returns
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for The Positive Similarity of Company Filings and Stock Returns in the browser. Results update live with equity, drawdown, and metrics charts. Allowed: backtest.data, backtest.engine, backtest.metrics, numpy, pandas. Define ASSETS and make_on_day(prices). Shortcut: Ctrl+Enter. API docs →
Quant Buffet syntax cheat sheet (copy / insert)
Paste these fragments into the editor. The sandbox rejects QuantConnect, os, and network libraries.
from __future__ import annotations
import numpy as np
import pandas as pd
from backtest.data import load_daily_prices
from backtest.engine import EngineConfig, PortfolioEngine
from backtest.metrics import compute_metricsASSETS = ["SPY", "QQQ", "TLT", "GLD", "BIL"]def make_on_day(prices: pd.DataFrame):
cols = [c for c in ASSETS if c in prices.columns]
sma = prices[cols].rolling(200, min_periods=200).mean()
state = {"last": None}
def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
if sma.loc[dt].isna().all():
return
key = (dt.year, dt.month)
if state["last"] == key:
return
state["last"] = key
long = [
s for s in cols
if pd.notna(prices.at[dt, s]) and pd.notna(sma.at[dt, s])
and prices.at[dt, s] > sma.at[dt, s]
]
weights = {} if not long else {s: 1.0 / len(long) for s in long}
engine.set_target_weights(dt, weights)
ready = sma.dropna(how="all").index.min() if sma.notna().any().any() else None
return on_day, readyengine.set_target_weights(dt, {"SPY": 0.60, "BIL": 0.40})Live backtest performance
Export to your platform
Transform Quant Buffet lab code (ASSETS + make_on_day / PortfolioEngine) into native classes for a third-party IDE — then copy and paste.
# Generated from Quant Buffet → QuantConnect LEAN
# Strategy: The Positive Similarity of Company Filings and Stock Returns
# Detected pattern: Absolute momentum
# Source uses Quant Buffet lab APIs (ASSETS + make_on_day / PortfolioEngine).
# Review fees, data, and risk before live trading — educational export only.
from AlgorithmImports import *
class QuantBuffetExport(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2010, 1, 1)
self.SetCash(100000)
tickers = ["SPY", "TLT", "GLD", "BIL"]
self.symbols = []
for t in tickers:
if "-" in t: # crypto proxy e.g. BTC-USD
self.symbols.append(self.AddCrypto(t.replace("-USD", ""), Resolution.Daily).Symbol)
else:
self.symbols.append(self.AddEquity(t, Resolution.Daily).Symbol)
self.Schedule.On(
self.DateRules.MonthStart(self.symbols[0]),
self.TimeRules.AfterMarketOpen(self.symbols[0], 30),
self.Rebalance,
)
# Logic: Long assets with positive 252-day return; equal-weight; monthly.
def Rebalance(self):
# Pattern: abs_momentum — Long assets with positive 252-day return; equal-weight; monthly.
# Default: equal-weight. Port your make_on_day weights here via SetHoldings.
w = 1.0 / len(self.symbols) if self.symbols else 0.0
for symbol in self.symbols:
self.SetHoldings(symbol, w)
Exported code uses the platform’s native classes and libraries. Install dependencies in your third-party IDE, then run. Validate before live trading.
Academic paper
The Positive Similarity of Company Filings and the Cross-Section of Stock Returns
Matus Padysak
- SKComenius University Bratislava
- ?Comenius University - Faculty of Mathematics, Physics and Informatics
- ?Quantpedia.com
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3690461
Strategy in a nutshell
The investment universe consists of stocks with large market cap covered by the Brain Company, for which stock prices were available to download from Yahoo Finance and had full history during the sample period. Firstly, only the similarity of the positive language is considered. The positive similarity score is calculated as the cosine similarity and is provided by the Brain Company. Each month, stocks are ranked based on the positive similarity language score of their most recent company filing and sorted into deciles. Long the bottom decile and short the top decile. The strategy is equally-weighted and rebalanced monthly.
Economic rationale
Firstly, there are major differences compared to Lazy prices. The presented paper is focused on the similarity of positive language only (compared to the similarity of all language), motivated by the search for the most profitable strategy. Additionally, the holding period is shorter (one month compared to three months), and stocks are sorted into deciles based on their most recent 10-K or 10-Q report. The strategy does not wait for the new quarter release, but rather use the most recent report. The last difference is the investment universe. Previous research has examined the effect on approximately 4000 stocks, which has to include smaller capitalization stocks and possible liquidity issues. The Brain analyzes company reports for approximately the largest 1000 US stocks. As a result, the investment universe includes mostly large caps with better liquidity and lower slippage costs and spreads.
The mechanism behind the functionality is foggy. The hypothesis of the paper is that the effort to change the positive language should positively influence subsequent returns because management does not have the motivation to change report if it would harm the company significantly. It should rather positively influence the potential investor.
Last but not least, results suggest that the low positive similarity effect is a distinct anomaly in the financial markets. There is an economically and statistically significant alpha, and also the change of sentiment extracted from filings cannot explain the positive similarity effect.