[This article was first published on DataGeeek, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.IntroductionPortfolio construction often relies on mean–variance optimization or factor models. Yet, recent research highlights the importance of skewness—the third statistical moment—as a driver of asset returns. Assets with lottery-like payoffs (high positive skewness) tend to be overpriced, while negatively skewed assets are often underpriced. A 66‑page study demonstrates that skewness‑managed portfolios consistently outperform traditional strategies, especially in volatile, short‑term horizons.Why Skewness MattersCaptures tail risk: Skewness measures asymmetry in return distributions, revealing whether extreme gains or losses dominate.Behavioral relevance: Investors are attracted to lottery‑like assets, creating systematic mispricing.Empirical evidence: Skewness‑managed portfolios deliver higher Sharpe ratios, particularly during recessions and high‑volatility regimes.MethodologyThe core idea is simple:Compute skewness of asset returns over a short horizon.Rank assets by skewness.Go long the top two, short the bottom two, and hold the rest.This rule, while straightforward, is supported by extensive empirical testing across anomalies, factor models, and macroeconomic cycles.Implementation in RBelow is a reproducible pipeline using tidyverse, tidyquant, and gt to construct a skewness‑managed portfolio:# Load required packageslibrary(tidyverse)library(tidyquant)library(timetk)library(moments)library(gt)# 1. Define symbolssymbols % mutate(ret = log(adjusted) - log(lag(adjusted))) %>% drop_na()# 3. Split data: last 15 days as test setsplit % # Create gt table gt() %>% # Add table header tab_header(title = "Skewness-Managed Portfolio (15-day Horizon)") %>% # Rename columns cols_label(asset_name = "Asset", skewness_pct = "Skewness (%)", position = "Portfolio Position") %>% # Make all column labels bold tab_style( style = cell_text(weight = "bold"), locations = cells_column_labels(columns = everything()) ) %>% # Align Asset column label to the left tab_style( style = cell_text(align = "left"), locations = cells_column_labels(columns = vars(asset_name)) ) %>% # Apply background colors based on portfolio position tab_style(style = cell_fill(color = "green"), locations = cells_body(columns = vars(position), rows = position == "Long")) %>% tab_style(style = cell_fill(color = "red"), locations = cells_body(columns = vars(position), rows = position == "Short")) %>% tab_style(style = cell_fill(color = "gray"), locations = cells_body(columns = vars(position), rows = position == "Hold")) %>% # Center align Skewness (%) and Portfolio Position columns tab_style(style = cell_text(align = "center", weight = "bold"), locations = cells_body(columns = vars(skewness_pct, position))) %>% # Left align Asset column values tab_style(style = cell_text(align = "left"), locations = cells_body(columns = vars(asset_name))) %>% # Add white borders between all cells tab_style(style = cell_borders(sides = "all", color = "white", weight = px(2)), locations = cells_body(columns = everything()))ConclusionSkewness‑managed portfolios provide a robust, statistically grounded way to exploit asymmetry in asset returns. While the rule is simple, the underlying research demonstrates its effectiveness across anomalies, macroeconomic regimes, and crisis periods. For short‑term, high‑frequency, and high‑volatility strategies, skewness management can be a powerful addition to the portfolio construction toolkit.To leave a comment for the author, please follow the link and comment on their blog: DataGeeek.R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.Continue reading: Skewness-Managed Portfolios: A Practical Guide with R