Macro Hive’s Post

🚨 Dominique Dwor-Frecaut Change of Call: 50bp Fed Cut in March 🇺🇸 🔸 Historically, core PCE has (almost) never been at 2.4% as the Fed expects in 2024; it has been either below 2% or much higher, which is consistent with a ‘regime’ view of inflation. 🔸 The SEP inflation scenario, if it materializes, is therefore likely to become a return to the pre-pandemic chronically underperforming inflation. 🔸 A return to ‘lowflation’ would see many more 2024 cuts than the 75bp in the SEP as the Fed’s resolve to fight it is likely as strong as before the pandemic. The first cut is probably in March because: -Likely low core PCE prints up to the March FOMC meeting will add to the risks of lowflation. -The Fed would rather be proactive in its fight against lowflation. -The Fed would rather cut well before the election season intensifies, which suggests cutting before H2. 🔸 On balance, I see a March 50bp cut as more likely than not. This because the Fed looks at policy in a probabilistic manner, weighing the risk of being too tight versus too loose. Read full analysis and Dominique's call here: https://lnkd.in/edg7kFyj

Check out our latest Key Events: Will Powell Acknowledge Progress on Disinflation? G10 In the US, the most important data is: -CPI – Thursday. Consensus expects core MoM CPI to remain at 0.2%. But as always, the services-good breakdown will be key to the inflation outlook. -PPI – Friday. Together with the CPI, the PPI will give an estimate of PCE (the more important inflation indicator for the Fed). In the Eurozone and UK, it is a light data week: -Final National EZ CPI – German (Thursday), France and Spain (Friday). Prelims showed services inflation momentum remained strong, but did wage-intensive sectors drive this? We expect momentum there will peak soon across EZ. -May UK Monthly GDP – Thursday. Expectations are for rise in MoM growth, led by a growth in services and a tick back up in manufacturing production. Elsewhere in G10: -Japan Wage Data – Monday. Pay attention to the same sample data for wage growth above 2%. Are above-inflation wage hikes for union workers boosting overall wage growth? -Australia NAB Business Survey – Tuesday. We continue to watch the forward orders component for signs of further demand contraction. Prices will be important, too. Read full article here: https://lnkd.in/e2TeJway

Check out our latest Key Events: Will the Moderate NFP Pattern Continue? G10 In the US, there is… -NFP – Friday. A repeat of last month’s pattern of moderate NFP with lower unemployment and faster wage growth could signal lower immigration flows and associated tightening of the labour market. -PMIs – Tuesday (manufacturing) and Thursday (services). PMIs are more important from a trading than from an economic perspective as they have decoupled from GDP growth. For market impact see our Event Monitor. In the Eurozone and UK, the main events will be: -Preliminary Germany and Italy September CPI – (Monday). Should add to the picture ahead of the EZ aggregate release (Tuesday). French and Spanish numbers came in softer than expected, suggesting there may be room for more ECB dovishness ahead if this is confirmed in the aggregate numbers. -Final September manufacturing (Tuesday) and services PMIs – (Thursday). Will be watching for whether it confirms the preliminary readings’ sharp slowdown. There, we saw deepening decline in manufacturing and marginal expansion in services, with August’s French Olympics boost having faded. Employment modestly reduced for the second month in a row, although it is growing in services. Input price inflation was slowest since late 2020 and output inflation was slowest since early 2021. Both sets saw deflation in goods and slowing inflation in services. -Eurozone unemployment – (Wednesday). Will be important to see whether there is any easing in the tightness of the labour market, which currently has the EZ hawks concerned. -BoE DMP survey – (Thursday). Will be important for details on business expectations for price setting as well as wage growth. We expect to continue to see normalisation in both, though this is not likely to move the dial much on monetary policy. The BoE seems to expect similar, with the hawks more concerned firm pricing power may be supported further down the line if consumption spend rebounds. Read full article here: https://lnkd.in/e9MTrMgP

Insightful Analysis on Fed Policy and Market Trends | Liz Ann Sonders https://lnkd.in/dFbjGJ5V #FedPolicy, #MarketTrends, #InflationFight, #EconomicAnalysis, #StockMarket, #RateHikes, #FedRateExpectations, #EconomicCycles, #PolicyEasing, #LizAnnSonders

Curious about the Fed's rate cut prospects? Erik Ogard breaks down the crucial data influencing the Fed's decision. Get insights on CPI, PPI, jobs data, and GDP in this must-watch update. #FinancialMarkets #Investing #FedRateCut #WealthManagement https://lnkd.in/gxQVA2cH

Data Confusion

🚀 Excited to share our latest project from ITCS 4122/5122 - Visual Analytics, led by Instructor Zhaocong Yang and supported by IAs Aneesh Reddy Edunuru and Bhumireddy Yasaswi. 📊 Technical Analysis Dashboard for Multiple Stocks is now live! This tool was meticulously developed to empower investors and traders with advanced visual analytics to conduct in-depth technical analysis of stock behaviors and trends. 👉 Explore the dashboard here: https://lnkd.in/diQubvhP GitHub: https://lnkd.in/dk_ECQaa Key Features: - Candlestick Charts: For detailed price movement visualization. - Bollinger Bands Chart: To assess market volatility. - Volume Charts: Highlight trading volumes and market sentiment. - Cycle Plots: Analyze seasonal and cyclical trading patterns. - Interactive Filters: Dive deep into specific datasets with real-time synchronization across charts. Usage: Identifying Trends: Use the candlestick chart with SMA overlays to spot emerging trends. Volatility Assessment: Look at the Bollinger Bands to gauge market volatility and identify potential buying or selling opportunities. Volume Analysis: Examine the volume chart and cycle plot to validate the strength behind price movements. Whether you're a novice looking to understand market dynamics or a seasoned trader devising complex strategies, this dashboard provides you with the tools you need to make informed decisions. We are proud of the work we've accomplished and excited to see how it can help demystify stock market analytics. We’d love to hear your thoughts and feedback! Contributors : Rahul Rajiv Yadav : Rahul Yadav Krista Sparks : Krista Sparks Mansi Akbari : Mansi Akbari Pallavi Deshmukh : Pallavi Deshmukh I will be adding more updates to these project so if anyone is interested in joining and working with me please reach out. #VisualAnalytics #TechnicalAnalysis #DataVisualization #StockMarket #Finance #Dashboard #UNCCharlotte #DataScience #Coding

DAX IMPORTANT QUESTION: 1. How does the ISBLANK function work in DAX? Can you give an example of when you would use ISBLANK to handle null or empty values in calculations? The ISBLANK function in DAX checks whether a value is blank or empty. It returns TRUE if the value is blank, and FALSE otherwise. Blank values in DAX can be the result of missing data or certain operations like division by zero. Example: Let's say you have a sales table where some customers haven't made any purchases yet, so the "Total Sales" column might be blank for those customers. You can use ISBLANK to replace the blank values with zero. Total Sales (No Blanks) = IF(ISBLANK([Total Sales]), 0, [Total Sales]) In this case, if Total Sales is blank, it will be replaced by 0, ensuring your reports don't display blank values. Example: If you are analyzing sales data, and some products have no sales recorded, ISBLANK helps avoid confusion by filling in zeros. This can improve the clarity of your reports, especially when creating visualizations that summarize sales by product. 2. How would you write a DAX expression to dynamically convert a measure's value into a different currency based on user selections or exchange rates? You can create a dynamic currency conversion in DAX by using measures and a currency conversion table that includes exchange rates. The user can select a currency, and the conversion will be applied based on the selected exchange rate. Steps: 1. Create a table of exchange rates with currencies like USD, EUR, etc. 2. Create a slicer in Power BI so users can choose a currency. 3. Write a DAX expression that looks up the exchange rate for the selected currency and applies it to your measure. Converted Sales =  VAR SelectedCurrency = SELECTEDVALUE(CurrencyTable[Currency]) VAR ExchangeRate = LOOKUPVALUE(CurrencyTable[ExchangeRate], CurrencyTable[Currency], SelectedCurrency) RETURN [Total Sales] * ExchangeRate Example: Imagine you're running a global e-commerce platform, and you want to show sales reports in different currencies depending on the region. By allowing users to choose their local currency from a slicer, you can convert the total sales dynamically based on real-time exchange rates, ensuring that users from different regions get accurate sales figures in their local currency. #PowerBI #DataModeling #SCD #DataWarehouse #DataManagement #DataHistory #DataAnalytics

Navigating Market Trends: Inside Bars, Balances, and B Formations in Futures Trading https://lnkd.in/dFbjGJ5V #FuturesTrading, #MarketTrends, #InsideBars, #MarketBalance, #BFormation, #TradingStrategies, #TechnicalAnalysis, #InvestmentInsights, #FinancialMarkets, #TradeTrend

Feature Elimination Using VIF VIF is a variance inflation factor that gives the measure of collinearity of features. VIF is a measure of how well a predictor variable is correlated with all the other variables, excluding the target variable. If the VIF value of a feature is high, the feature can be represented as a combination of the other features. The features that can be represented as a combination of other features do not add information to the data, and they need to be eliminated in order to reduce noise. So you need to check the variance inflation factors (VIFs) to eliminate the redundant variables further. VIF calculates how well one independent variable is explained by all the other independent variables combined. And its formula is as follows:   Here, 'i' refers to the ith variable, which is being represented as a combination of the rest of the independent variables. A regression model is run with xi as the dependent variable, and all the other features are the independent variables. This regression model will give an R_square value, which is used to calculate the VIF of the feature xi.The closer the value of R_square is to 1, the higher the VIF value is . A high VIF value signifies that a linear combination of features can describe the xi. Here is the common heuristic we follow for VIF values: VIF >10 : VIF value is definitely high, and the variable should be eliminated. 5< VIF <10: This VIF value can be okay, but it is worth inspecting. VIF <5: This is a good VIF value. No need to eliminate this variable. Note that the threshold we use for VIF is 5, i.e., the R-squared value is 0.8 or that almost 80% of the variance of a variable is explained by other variables present in the model and is highly correlated. Hence, you only want to retain variables whose VIF is 5 or lower. It is always advisable that you drop variables one by one.It might happen that after you drop one, for other variables their VIF values will drop. So never drop more than one variable at a time. [224]: # Check for the VIF values of the feature variables. from statsmodels.stats.outliers_influence import variance_inflation_factor [225]: