diff --git a/data/papers.json b/data/papers.json new file mode 100644 index 0000000..5397c24 --- /dev/null +++ b/data/papers.json @@ -0,0 +1,17 @@ +[ + { + "title": "Bearish or Bullish", + "githubLink": "https://github.com/Matts52/Bearish-or-Bullish", + "imageSrc": "assets/projects/Bearish_or_Bullish.jpg", + "description": "An exploratory analysis of the predictive capacity of Wall Street Journal language attention and sentiment on key market fluctuation indicators. This paper first scraped a vast database of WSJ articles, then built an LDA model for the language used, while using the RoBERTa sentiment transformer to weigh directional attention of topics. Finally a Lasso regression was applied to optimize the bias-variance tradeoff with respect to how many topics to include when predicting movement of market fluctuation indicators.", + "pdfLink": "assets/papers/Bearish_or_Bullish.pdf" + }, + { + "title": "Money is Motivation", + "githubLink": "https://github.com/Matts52/Money-is-Motivation", + "imageSrc": "assets/projects/Money_is_Motivation.jpg", + "description": "An investigation of the game-level predictive capacity of intrateam salary dispersion. I utilize NBA salary metrics and scrape NBA game minute logs to create measures of per-minute salary dispersion which is then used to predict game-level outcomes using Random Forests. This is all done as a proxy for the future-looking short-term performance of a firm with respect to within company pay distribution.", + "pdfLink": "assets/papers/Money_is_Motivation.pdf" + } + ] + \ No newline at end of file diff --git a/scripts/gen_tiles.js b/scripts/gen_tiles.js index a201cdc..3482087 100644 --- a/scripts/gen_tiles.js +++ b/scripts/gen_tiles.js @@ -43,24 +43,11 @@ async function generateProjectTiles(){ -function generatePaperTiles() { - // Define an array of paper data - const papers = [ - { - title: "Bearish or Bullish", - githubLink: "https://github.com/Matts52/Bearish-or-Bullish", - imageSrc: "assets/projects/Bearish_or_Bullish.jpg", - description: "An exploratory analysis of the predictive capacity of Wall Street Journal language attention and sentiment on key market fluctuation indicators. This paper first scraped a vast database of WSJ articles, then built an LDA model for the language used, while using the RoBERTa sentiment transformer to weigh directional attention of topics. Finally a Lasso regression was applied to optimize the bias-variance tradeoff with respect to how many topics to include when predicting movement of market fluctuation indicators.", - pdfLink: "assets/papers/Bearish_or_Bullish.pdf", - }, - { - title: "Money is Motivation", - githubLink: "https://github.com/Matts52/Money-is-Motivation", - imageSrc: "assets/projects/Money_is_Motivation.jpg", - description: "An investigation of the game-level predictive capacity of intrateam salary dispersion. I utilize NBA salary metrics and scrape NBA game minute logs to create measures of per-minute salary dispersion which is then used to predict game-level outcomes using Random Forests. This is all done as a proxy for the future-looking short-term performance of a firm with respect to within company pay distribution.", - pdfLink: "assets/papers/Money_is_Motivation.pdf", - }, - ]; +async function generatePaperTiles() { + // Fetch paper data from JSON file + const response = await fetch('data/papers.json'); + const papers = await response.json(); + // Get the container element const container = document.querySelector("#papers");